#hide
!pip install nbdev
from nbdev import *
! nbdev_upgrade<h2>Introduction</h2>
<p><a href="https://mybinder.org/v2/gh/karpatic/datalab/master?filepath=%2Fnotebooks%2F02_scooterExploration.ipynb"><img src="https://mybinder.org/badge_logo.svg" alt="Binder" /></a>
<a href="https://colab.research.google.com/github/karpatic/datalab/blob/master/notebooks/02_scooterExploration.ipynb"><img src="https://pete88b.github.io/fastpages/assets/badges/colab.svg" alt="Binder" /></a>
<a href="https://github.com/karpatic/datalab/tree/master/notebooks/02_scooterExploration.ipynb"><img src="https://pete88b.github.io/fastpages/assets/badges/github.svg" alt="Binder" /></a>
<a href="https://github.com/ellerbrock/open-source-badges/"><img src="https://badges.frapsoft.com/os/v3/open-source.svg?v=103" alt="Open Source Love svg3" /></a></p>
<p><a href="https://github.com/karpatic/datalabs/blob/master/LICENSE"><img src="https://img.shields.io/npm/l/all-contributors.svg?style=flat" alt="NPM License" /></a>
<a href="https://karpatic.github.io"><img src="http://img.shields.io/badge/Status-Active-green.svg" alt="Active" /></a>
<a href="https://pypi.python.org/pypi/dataplay/"><img src="https://img.shields.io/pypi/pyversions/dataplay.svg" alt="Python Versions" /></a>
<a href=""><img src="https://img.shields.io/github/last-commit/karpatic/datalabs.svg?style=flat" alt="GitHub last commit" /></a>
<a href="http://unmaintained.tech/"><img src="http://unmaintained.tech/badge.svg" alt="No Maintenance Intended" /></a> </p>
<p><a href="https://github.com/karpatic/datalabs"><img src="https://img.shields.io/github/stars/karpatic/datalabs.svg?style=social&amp;label=Star" alt="GitHub stars" /></a>
<a href="https://github.com/karpatic/datalabs"><img src="https://img.shields.io/github/watchers/karpatic/datalabs.svg?style=social&amp;label=Watch" alt="GitHub watchers" /></a>
<a href="https://github.com/karpatic/datalabs"><img src="https://img.shields.io/github/forks/karpatic/datalabs.svg?style=social&amp;label=Fork" alt="GitHub forks" /></a>
<a href="https://github.com/karpatic/datalabs"><img src="https://img.shields.io/github/followers/karpatic.svg?style=social&amp;label=Follow" alt="GitHub followers" /></a> </p>
<p><a href="https://twitter.com/intent/tweet?text=Check%20out%20this%20%E2%9C%A8%20colab%20by%20@bniajfi%20https://github.com/karpatic/datalabs%20%F0%9F%A4%97"><img src="https://img.shields.io/twitter/url/https/github.com/karpatic/datalabs.svg?style=social" alt="Tweet" /></a>
<a href="https://twitter.com/bniajfi"><img src="https://img.shields.io/twitter/follow/bniajfi.svg?style=social" alt="Twitter Follow" /></a></p>
<p>This chapter has brought to you in collaboration by Brian Kelly, Michael Vandi, Logan Shertz, and Charles Karpati.</p>
<p><strong>Dataset:</strong>
Scooter data: </p>
<ul>
<li>Routes: 3 months (September to August 2019)</li>
<li>Deployment/</li>
<li>Routes</li>
<li>Trip origins-destinations by month</li>
</ul>
# do you see this? - Carlos# Hello world! - Michael# Checking - Brian<h2>Local File Access (Optional)</h2>
# hide_output
# (Optional) Run this cell to gain access to Google Drive (Colabs only) 
from google.colab import drive

# Colabs operates in a virtualized enviornment
# Colabs default directory is at ~/content.
# We mount Drive into a temporary folder at '~/content/drive' 

drive.mount('/content/drive')Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly&response_type=code

Enter your authorization code:
··········
Mounted at /content/drive
<p>File path's will vary</p>
# cd ./drive/'My Drive'/BNIA/responsive_records/Routes# cd ../content/drive/My Drive/DATA/scooter/content/drive/My Drive/DATA/scooter
# Michael's Directory
# cd ./drive/'My Drive'/BNIA/'Scooter Use Data'/BNIAcd ./Routes/content/drive/My Drive/DATA/scooter/Routes
# hide_output
! ls leftRouts.csv			 'Routing November 2019.geojson'
 rightRouts.csv			 'Routing October 2019.geojson'
'Routing August 2019.geojson'	 'Routing September 2019.geojson'
'Routing December 2019.geojson'
# hide_output
!cd ../ && ls boundsdf.csv	 rightRouts.csv  'Trip origins-destinations by month'
 Deployment	 Routes
 leftRouts.csv	 scooterdf.csv
<h1><strong>Installs</strong></h1>
! pip install dexplot
!pip install folium
!pip install geopandas
!pip install ipyleaflet
!pip install gpdvega
!pip install dataplay<h1><strong>Imports</strong></h1>
import os
import pandas as pd
import geopandas as gpd
import dexplot as dxp
import folium as fol
import json
import altair as alt
import gpdvega
import dataplay
# These imports will handle everything
import os
import sys
import csv
from IPython.display import clear_output
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import geopandas as gpd
from geopandas import GeoDataFrame
import psycopg2
import pyproj
from pyproj import Proj, transform
# conda install -c conda-forge proj4
from shapely.geometry import Point
from shapely import wkb
from shapely.wkt import loads
# https://pypi.org/project/geopy/
from geopy.geocoders import Nominatim
import folium
from folium import plugins

from dataplay.merge import mergeDatasets 

import dexplot as dxp

# In case file is KML, enable support
import fiona
fiona.drvsupport.supported_drivers['kml'] = 'rw'
fiona.drvsupport.supported_drivers['KML'] = 'rw'

#this cell is good to copy
from shapely.geometry import LineString
pd.plotting.register_matplotlib_converters()

from dataplay.geoms import readInGeometryData
from shapely import wkt
from dataplay.geoms import readInGeometryData

import ipywidgets as widgets
!jupyter nbextension enable --py widgetsnbextension
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = 'all'
import ipywidgets as widgets
from ipywidgets import interact, interact_manual
alt.data_transformers.enable('default', max_rows=None)<h3>Convenience Functions</h3>
# Return the boundaries of a Linestring 
def split(geom):
  print( str( type(geom)) )
  #Linestring might not be the actual type, so this may need to be fied. 
  #IF its not a linestring then dont run it and stuff 'false' and the datatype
  if str( type(geom)) == "<class 'shapely.geometry.linestring.LineString'>" and not str(geom.boundary) == 'MULTIPOINT EMPTY':
    print(geom.boundary)
    left, right = geom.boundary
    print('left x: ', type(left.x), left.x)
    print('left y: ', type(left.y), left.y)
    print('right x: ', type(right.x), right.x)
    print('right y: ', type(right.y), right.y)
    return left.x, left.y, right.x, right.y
  else: return False, type(geom), False, type(geom)# To 'import' a script you wrote, map its filepath into the sys
def getPointsInPolygons(pts, polygons, ptsCoordCol, polygonsCoordCol): 
    count = 0 
    total = pts.size / len(pts.columns)
    # We're going to keep a list of how many points we find.
    pts_in_polys = []

    # Loop over polygons with index i.
    for i, poly in polygons.iterrows():
        # print('Searching for point within Geom:', poly )
        # Keep a list of points in this poly
        pts_in_this_poly = []

        # Now loop over all points with index j.
        for j, pt in pts.iterrows():
          if poly[polygonsCoordCol].contains(pt[ptsCoordCol]):
            # Then it's a hit! Add it to the list,
            # and drop it so we have less hunting.
            pts_in_this_poly.append(pt[ptsCoordCol])
            pts = pts.drop([j])

        # We could do all sorts, like grab a property of the
        # points, but let's just append the number of them.
        pts_in_polys.append(len(pts_in_this_poly))
        print('Found this many points within the Geom:', len(pts_in_this_poly) ) 
        count = count + len(pts_in_this_poly) 
        clear_output(wait=True)

    # Add the number of points for each poly to the dataframe.
    polygons['pointsinpolygon'] = gpd.GeoSeries(pts_in_polys)
    print( 'Total Points: ', total )
    print( 'Total Points in Polygons: ', count )
    print( 'Prcnt Points in Polygons: ', count / total )
    return polygons<h3>File Access Convenience Functions</h3>
# Find Relative Path to Files
def findFile(root, file):
    for d, subD, f in os.walk(root):
        if file in f:
            return "{1}/{0}".format(file, d)
            break 

# To 'import' a script you wrote, map its filepath into the sys
def addPath(root, file): sys.path.append(os.path.abspath( findFile( './', file) ))findFile('./', 'Routing September 2019.geojson')'.//Routing September 2019.geojson'ls leftRouts.csv                   'Routing November 2019.geojson'
 rightRouts.csv                  'Routing October 2019.geojson'
'Routing August 2019.geojson'    'Routing September 2019.geojson'
'Routing December 2019.geojson'
<h2>Inspect Deployment Data</h2>
ls 'Trip origins-destinations by month''Trip Destinations by block August 2019.geojson'
'Trip Destinations by block December 2019.geojson'
'Trip Destinations by block November 2019.geojson'
'Trip Destinations by block October 2019.geojson'
'Trip Destinations by block September 2019.geojson'
'Trip Origins by block August 2019.geojson'
'Trip Origins by block December 2019.geojson'
'Trip Origins by block November 2019.geojson'
'Trip Origins by block October 2019.geojson'
'Trip Origins by block September 2019.geojson'
#@title Example form fields
#@markdown Forms support many types of fields.
fileName = "Trip Origins by block September 2019.geojson"  #@param ['Daily Deployment average by block August 2019.csv', 'Daily Deployment average by block December 2019.csv', 'Daily Deployment average by block November 2019.csv', 'Daily Deployment average by block October 2019.csv', 'Daily Deployment average by block September 2019.csv', 'Trip Destinations by block August 2019.geojson','Trip Destinations by block December 2019.geojson','Trip Destinations by block November 2019.geojson', 'Trip Destinations by block October 2019.geojson', 'Trip Destinations by block September 2019.geojson', 'Trip Origins by block August 2019.geojson','Trip Origins by block December 2019.geojson','Trip Origins by block November 2019.geojson','Trip Origins by block October 2019.geojson','Trip Origins by block September 2019.geojson']
#@markdown ---gdf = gpd.read_file( findFile('./', fileName) )
gdf.head()<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>id</th>
      <th>name</th>
      <th>color</th>
      <th>radius</th>
      <th>value</th>
      <th>geometry</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>1</td>
      <td>Block 245102502063018</td>
      <td>#fff</td>
      <td>4.999286</td>
      <td>NaN</td>
      <td>POLYGON ((-76.66168 39.26342, -76.66136 39.26373, -76.66096 39.26343, -76.66041 39.26306, -76.66053 39.26296, -76.66070 39.26281, -76.66077 39.26284, -76.66100 39.26296, -76.66117 39.26306, -76.66129 39.26315, -76.66141 39.26322, -76.66168 39.26342))</td>
    </tr>
    <tr>
      <th>1</th>
      <td>2</td>
      <td>Block 245102006001022</td>
      <td>#fff</td>
      <td>4.999286</td>
      <td>NaN</td>
      <td>POLYGON ((-76.66319 39.28427, -76.66306 39.28436, -76.66301 39.28439, -76.66293 39.28439, -76.66271 39.28418, -76.66303 39.28402, -76.66317 39.28417, -76.66320 39.28423, -76.66319 39.28427))</td>
    </tr>
    <tr>
      <th>2</th>
      <td>3</td>
      <td>Block 245102804033017</td>
      <td>#fff</td>
      <td>4.999286</td>
      <td>NaN</td>
      <td>POLYGON ((-76.71122 39.27927, -76.71121 39.27975, -76.71121 39.27990, -76.71119 39.28076, -76.71045 39.27932, -76.71045 39.27920, -76.71056 39.27907, -76.71123 39.27880, -76.71122 39.27927))</td>
    </tr>
    <tr>
      <th>3</th>
      <td>4</td>
      <td>Block 245102716006003</td>
      <td>#fff</td>
      <td>4.999286</td>
      <td>NaN</td>
      <td>POLYGON ((-76.66960 39.34512, -76.66905 39.34526, -76.66886 39.34531, -76.66848 39.34539, -76.66835 39.34502, -76.66867 39.34498, -76.66924 39.34484, -76.66946 39.34475, -76.66960 39.34512))</td>
    </tr>
    <tr>
      <th>4</th>
      <td>5</td>
      <td>Block 245102709023013</td>
      <td>#fff</td>
      <td>4.999286</td>
      <td>NaN</td>
      <td>POLYGON ((-76.58976 39.35401, -76.58972 39.35415, -76.58966 39.35435, -76.58964 39.35447, -76.58960 39.35469, -76.58940 39.35467, -76.58925 39.35465, -76.58912 39.35465, -76.58902 39.35464, -76.58898 39.35464, -76.58906 39.35425, -76.58917 39.35385, -76.58925 39.35385, -76.58939 39.35386, -76.58979 39.35393, -76.58976 39.35401))</td>
    </tr>
  </tbody>
</table>
</div>gdf.plot(column = 'value')<matplotlib.axes._subplots.AxesSubplot at 0x7f2c97160da0><Figure size 432x288 with 1 Axes>point_df = gdf.copy()
point_df['centroids'] = df.centroid
point_df = point_df.drop(columns = 'geometry')
point_df = point_df.set_geometry('centroids')
point_df.head(1)
point_df.plot(marker='o', color='red', markersize='value')point_df.value.hist()point_df[point_df.value > 1000].plot()baltimore = gpd.read_file("https://opendata.arcgis.com/datasets/b738a8587b6d479a8824d937892701d8_0.geojson")from dataplay.geoms import workWithGeometryDatabaltimore.columnsIndex(['OBJECTID', 'CSA2010', 'tpop10', 'male10', 'female10', 'paa17',
       'pwhite17', 'pasi17', 'p2more17', 'ppac17', 'phisp17', 'racdiv17',
       'age5_17', 'age18_17', 'age24_17', 'age64_17', 'age65_17', 'hhs10',
       'femhhs17', 'fam17', 'hhsize10', 'mhhi17', 'hh25inc17', 'hh40inc17',
       'hh60inc17', 'hh75inc17', 'hhm7517', 'hhpov17', 'hhchpov17',
       'Shape__Area', 'Shape__Length', 'geometry'],
      dtype='object')pointsInPolys = workWithGeometryData(method = 'ponp', df = point_df, polys = baltimore, ptsCoordCol ='centroids', 
                                     polygonsCoordCol = 'geometry', polygonsLabel = 'CSA2010')
pointsInPolys.plot(column='value', legend=True, markersize = 1)Total Points:  13506.0
Total Points in Polygons:  13453
Prcnt Points in Polygons:  0.9960758181548941
<matplotlib.axes._subplots.AxesSubplot at 0x7ff7c35c92e8><Figure size 432x288 with 2 Axes>ponp_copy = pointsInPolys.copy()ponp_copy.value.isnull().groupby([ponp_copy['CSA2010']]).sum().astype(int).reset_index(name='NumberMissingCount').head()<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>CSA2010</th>
      <th>NumberMissingCount</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>Allendale/Irvington/S. Hilton</td>
      <td>290</td>
    </tr>
    <tr>
      <th>1</th>
      <td>Beechfield/Ten Hills/West Hills</td>
      <td>180</td>
    </tr>
    <tr>
      <th>2</th>
      <td>Belair-Edison</td>
      <td>250</td>
    </tr>
    <tr>
      <th>3</th>
      <td>Brooklyn/Curtis Bay/Hawkins Point</td>
      <td>580</td>
    </tr>
    <tr>
      <th>4</th>
      <td>Canton</td>
      <td>0</td>
    </tr>
  </tbody>
</table>
</div>ponp_copy.value.notnull().groupby([ponp_copy['CSA2010']]).sum().astype(int) / ponp_copy.value.isnull().groupby([ponp_copy['CSA2010']]).sum().astype(int)ponp_copy.value.isnull().groupby([ponp_copy['CSA2010']]).sum().astype(int) / ponp_copy.value.groupby([ponp_copy['CSA2010']]).sum().astype(int) * 100ponp_copy.fillna(-1).groupby('CSA2010')['value'].sum()gdf.value.unique()gdf.value.describe()count    5677.000000
mean     40.862603  
std      175.769408 
min      1.000000   
25%      2.000000   
50%      5.000000   
75%      21.000000  
max      6978.000000
Name: value, dtype: float64gdf.value.var() # Return unbiased variance over requested axis.30894.884924369213gdf.value.sem() # Return unbiased standard error of the mean over requested axis.2.332834040372481gdf.value.nunique()	# Count distinct observations over requested axis.364# DataFrame.shape	Return a tuple representing the dimensionality of the DataFrame.
gdf.shape

#DataFrame.size	Return an int representing the number of elements in this object.
gdf.size

# DataFrame.ndim	Return an integer representing the number of axes/array dimensions.
gdf.ndim

# Note Used : 
# DataFrame.axes	Return a list representing the axes of the DataFrame.

gdf.dtypes

# Return unbiased kurtosis over requested axis using Fisher’s definition of kurtosis (kurtosis of normal == 0.0).
gdf.kurtosis()(13506, 6)810362id          object  
name        object  
color       object  
radius      float64 
value       float64 
geometry    geometry
dtype: objectid       -1.200000   
radius    1132.851562
value     495.784213 
dtype: float64<h3>Load Tracts</h3>
# This will just beautify the output.

pd.set_option('display.expand_frame_repr', False)
pd.set_option('display.precision', 2)
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"

pd.set_option('max_colwidth', 20)# The attributes are what we will use.
in_crs = 2248 # The CRS we recieve our data.
out_crs = 4326 # The CRS we would like to have our data represented as.
geom = 'geometry' # The column where our spatial information lives.

# A Url to load
boundariesBaltimoreTractsNoWater2010 = "https://docs.google.com/spreadsheets/d/e/2PACX-1vQ8xXdUaT17jkdK0MWTJpg3GOy6jMWeaXTlguXNjCSb8Vr_FanSZQRaTU-m811fQz4kyMFK5wcahMNY/pub?gid=886223646&single=true&output=csv"

# Read in the dataframe
gdf = pd.read_csv(boundariesBaltimoreTractsNoWater2010)

# Convert the geometry column datatype from a string of text into a coordinate datatype.
gdf['geometry'] = gdf['geometry'].apply(lambda x: loads( str(x) ))

# Process the dataframe as a geodataframe with a known CRS and geom column.
gdf = GeoDataFrame(gdf, crs=in_crs, geometry='geometry')boundariesBaltimoreTractsNoWater2010.head()<p>Ensure merge is on consistent datatypes</p>
gdf['GEOID10'] = gdf['GEOID10'].astype(str)
scooterdf['nameChange2'] = scooterdf['nameChange2'].astype(str)<p>Perform the merge</p>
scooterdfClean = gdf.merge(scooterdf, left_on='GEOID10', right_on='nameChange2').drop(['name', 'nameChange1', 'nameChange2'], axis=1)scooterdfClean.head()# scooterdf.to_csv('./scooterdf.csv', index=False)
# gdf.drop(columns='geometry').to_csv('./boundsdf.csv', index=False)lsscooterdfClean.groupby('CSA')['value'].sum()scooterdfClean.value.isna().groupby([scooterdfClean['CSA']]).sum().astype(int).reset_index(name='notApplicable')scooterdfClean.value.notnull().groupby([scooterdfClean['CSA']]).sum().astype(int).reset_index(name='NotMissingCount')scooterdfClean.value.isnull().groupby([scooterdfClean['CSA']]).sum().astype(int).reset_index(name='NumberMissingCount')scooterdfClean.fillna(-1).groupby('CSA')['value'].sum()scooterdfClean.groupby('CSA')['value'].mean()scooterdfClean.groupby('CSA')['value'].sum()scooterdfClean.CSA.value_counts()https://pandas.pydata.org/docs/getting_started/intro_tutorials/06_calculate_statistics.html<h2>Inspect Routes Data</h2>
fileName = 'Routing December 2019.geojson'  #@param ['Routing August 2019.geojson', 'Routing October 2019.geojson', 'Routing December 2019.geojson', 'Routing September 2019.geojson', 'Routing November 2019.geojson']
columnName = "streetname"  #@param ['id', 'color', 'streetname', 'trip_count_sum', 'trip_count_average', 'trip_count_percent']
gdf = gpd.read_file( findFile('./', fileName) )
gdf.head()<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>id</th>
      <th>color</th>
      <th>streetname</th>
      <th>trip_count_sum</th>
      <th>trip_count_average</th>
      <th>trip_count_percent</th>
      <th>geometry</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>150197772</td>
      <td>rgb(218, 231, 241)</td>
      <td>Jefferson Street</td>
      <td>24</td>
      <td>0.774194</td>
      <td>0.0%</td>
      <td>LINESTRING (-76.58576 39.29660, -76.58550 39.29661)</td>
    </tr>
    <tr>
      <th>1</th>
      <td>150155955</td>
      <td>rgb(164, 190, 219)</td>
      <td></td>
      <td>206</td>
      <td>6.645161</td>
      <td>0.3%</td>
      <td>LINESTRING (-76.61320 39.28136, -76.61318 39.28115)</td>
    </tr>
    <tr>
      <th>2</th>
      <td>150191673</td>
      <td>rgb(204, 221, 236)</td>
      <td>Harford Avenue</td>
      <td>49</td>
      <td>1.580645</td>
      <td>0.1%</td>
      <td>LINESTRING (-76.60169 39.30535, -76.60163 39.30544, -76.60156 39.30555)</td>
    </tr>
    <tr>
      <th>3</th>
      <td>150184657</td>
      <td>rgb(229, 239, 246)</td>
      <td></td>
      <td>11</td>
      <td>0.354839</td>
      <td>0.0%</td>
      <td>LINESTRING (-76.61493 39.29294, -76.61491 39.29258, -76.61490 39.29230)</td>
    </tr>
    <tr>
      <th>4</th>
      <td>150188407</td>
      <td>rgb(169, 194, 221)</td>
      <td></td>
      <td>178</td>
      <td>5.741935</td>
      <td>0.2%</td>
      <td>LINESTRING (-76.61662 39.29053, -76.61661 39.29046)</td>
    </tr>
  </tbody>
</table>
</div>baltimore = gpd.read_file("https://opendata.arcgis.com/datasets/b738a8587b6d479a8824d937892701d8_0.geojson")tst = """background = alt.Chart(gdf).mark_geoshape(
    stroke='white',
    strokeWidth=2
).encode(
    color=alt.value('#eee'),
).properties(
    width=700,
    height=500
)"""

# GeoDataFrame could be passed as usual pd.DataFrame
city = alt.Chart(baltimore).mark_geoshape(
).project(
).encode(
    color='tpop10', # shorthand infer types as for regular pd.DataFrame
    tooltip='CSA2010' # GeoDataFrame.index is accessible as id
).properties(
    width=500,
    height=300
)

routes = alt.Chart(gdf).mark_geoshape(
    filled=False,
    strokeWidth=2
)

city + routes<p>Clean the gdf of empties.</p>
gdf = gdf[~gdf.isna()]
gdf = gdf[~gdf.is_empty]<p>Now get the extremities; this will take a minute.</p>
# hide_output
gdf['lefty'], gdf['leftx'],gdf['righty'], gdf['rightx'] = zip(*gdf["geometry"].map(split))<p>Split the gdf into a left and right dataset</p>
gdf_left = gdf.copy()
gdf_left = gdf_left.drop(columns = ['geometry','rightx', 'righty'])

gdf_right= gdf.copy()
gdf_right = gdf_right.drop(columns = ['geometry','leftx', 'lefty', 'streetname', 'trip_count_sum', 'trip_count_average', 'trip_count_percent', 'color' ])<p>The coordinate variables of object type will cause problems.</p>
gdf_right.dtypesid        int64  
righty    float64
rightx    object 
dtype: object<p>Let's go ahead and coerce a correction.</p>
gdf_right['righty']=pd.to_numeric(gdf_right['righty'], errors='coerce')
gdf_left['lefty']=pd.to_numeric(gdf_left['lefty'], errors='coerce')

gdf_right['rightx']=pd.to_numeric(gdf_right['rightx'], errors='coerce')
gdf_left['leftx']=pd.to_numeric(gdf_left['leftx'], errors='coerce')<p>Now we can view the results</p>
gdf_right.dtypesid        int64  
righty    float64
rightx    float64
dtype: object<p>Save these csv's because it took a minute to get to where we are now.</p>
gdf_right.to_csv('rightRouts.csv')
gdf_left.to_csv('leftRouts.csv')<p>Convert the datasets to geodataframes for further exploration!</p>
# We could create a geodataframe like this
#temp_gdf = gpd.GeoDataFrame( gdf_right, geometry=gpd.points_from_xy(gdf_right.rightx, gdf_right.righty) )
#temp_gdf.head()

# Alternately this could work.. unfinished.. but wkt.loads can make a Point from text
# gdf_right['strCol']=gdf_right['rightx'].astype(str)
# gdf_right['geometry'] = gdf_right['strCol'].apply(wkt.loads)# hide_output
left_csaMap = readInGeometryData(url=gdf_left, porg='p', geom= False, lat= 'leftx', lng= 'lefty', revgeocode=False, save=False, in_crs=4268, out_crs=4268)# hide_output
right_csaMap = readInGeometryData(url=gdf_right, porg='p', geom= False, lat= 'rightx', lng= 'righty', revgeocode=False, save=False, in_crs=4268, out_crs=4268)right_csaMap.head()<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>id</th>
      <th>righty</th>
      <th>rightx</th>
      <th>geometry</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>150197772</td>
      <td>-76.585503</td>
      <td>39.296607</td>
      <td>POINT (-76.58550 39.29661)</td>
    </tr>
    <tr>
      <th>1</th>
      <td>150155955</td>
      <td>-76.613183</td>
      <td>39.281147</td>
      <td>POINT (-76.61318 39.28115)</td>
    </tr>
    <tr>
      <th>2</th>
      <td>150191673</td>
      <td>-76.601555</td>
      <td>39.305545</td>
      <td>POINT (-76.60156 39.30555)</td>
    </tr>
    <tr>
      <th>3</th>
      <td>150184657</td>
      <td>-76.614899</td>
      <td>39.292301</td>
      <td>POINT (-76.61490 39.29230)</td>
    </tr>
    <tr>
      <th>4</th>
      <td>150188407</td>
      <td>-76.616608</td>
      <td>39.290458</td>
      <td>POINT (-76.61661 39.29046)</td>
    </tr>
  </tbody>
</table>
</div>left_csaMap.head()left_csaMap.plot(column='trip_count_sum')right_csaMap.plot()baltimore.columns# hide_output
right_points_in_poly = getPointsInPolygons(right_csaMap, baltimore, 'geometry', 'geometry')right_csaMap.head()left_points_in_poly = getPointsInPolygons(left_csaMap, baltimore, 'geometry', 'geometry')left_csaMap.head()left_points_in_poly.drop('geometry', axis=1)[['pointsinpolygon']].head(20)<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

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        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>pointsinpolygon</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>18</td>
    </tr>
    <tr>
      <th>1</th>
      <td>0</td>
    </tr>
    <tr>
      <th>2</th>
      <td>67</td>
    </tr>
    <tr>
      <th>3</th>
      <td>0</td>
    </tr>
    <tr>
      <th>4</th>
      <td>1536</td>
    </tr>
    <tr>
      <th>5</th>
      <td>18</td>
    </tr>
    <tr>
      <th>6</th>
      <td>32</td>
    </tr>
    <tr>
      <th>7</th>
      <td>8</td>
    </tr>
    <tr>
      <th>8</th>
      <td>1</td>
    </tr>
    <tr>
      <th>9</th>
      <td>65</td>
    </tr>
    <tr>
      <th>10</th>
      <td>0</td>
    </tr>
    <tr>
      <th>11</th>
      <td>0</td>
    </tr>
    <tr>
      <th>12</th>
      <td>13</td>
    </tr>
    <tr>
      <th>13</th>
      <td>1764</td>
    </tr>
    <tr>
      <th>14</th>
      <td>0</td>
    </tr>
    <tr>
      <th>15</th>
      <td>760</td>
    </tr>
    <tr>
      <th>16</th>
      <td>0</td>
    </tr>
    <tr>
      <th>17</th>
      <td>85</td>
    </tr>
    <tr>
      <th>18</th>
      <td>1253</td>
    </tr>
    <tr>
      <th>19</th>
      <td>2</td>
    </tr>
  </tbody>
</table>
</div>right_points_in_poly.drop('geometry', axis=1)[['pointsinpolygon']].head(20)<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

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        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>pointsinpolygon</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>18</td>
    </tr>
    <tr>
      <th>1</th>
      <td>0</td>
    </tr>
    <tr>
      <th>2</th>
      <td>67</td>
    </tr>
    <tr>
      <th>3</th>
      <td>0</td>
    </tr>
    <tr>
      <th>4</th>
      <td>1536</td>
    </tr>
    <tr>
      <th>5</th>
      <td>18</td>
    </tr>
    <tr>
      <th>6</th>
      <td>32</td>
    </tr>
    <tr>
      <th>7</th>
      <td>8</td>
    </tr>
    <tr>
      <th>8</th>
      <td>1</td>
    </tr>
    <tr>
      <th>9</th>
      <td>65</td>
    </tr>
    <tr>
      <th>10</th>
      <td>0</td>
    </tr>
    <tr>
      <th>11</th>
      <td>0</td>
    </tr>
    <tr>
      <th>12</th>
      <td>13</td>
    </tr>
    <tr>
      <th>13</th>
      <td>1764</td>
    </tr>
    <tr>
      <th>14</th>
      <td>0</td>
    </tr>
    <tr>
      <th>15</th>
      <td>760</td>
    </tr>
    <tr>
      <th>16</th>
      <td>0</td>
    </tr>
    <tr>
      <th>17</th>
      <td>85</td>
    </tr>
    <tr>
      <th>18</th>
      <td>1253</td>
    </tr>
    <tr>
      <th>19</th>
      <td>2</td>
    </tr>
  </tbody>
</table>
</div>left_points_in_poly.plot(column='pointsinpolygon', legend=True)<matplotlib.axes._subplots.AxesSubplot at 0x7fca535fbbe0><Figure size 432x288 with 2 Axes>right_points_in_poly.plot(column='pointsinpolygon', legend=True)<matplotlib.axes._subplots.AxesSubplot at 0x7fca51773978><Figure size 432x288 with 2 Axes>right_points_in_polyscooterdfClean = left_points_in_poly.merge(right_points_in_poly, left_on='CSA2010', right_on='CSA2010', how='left')scooterdfClean.columnsright_points_in_poly.columnsIndex(['OBJECTID', 'CSA2010', 'tpop10', 'male10', 'female10', 'paa17',
       'pwhite17', 'pasi17', 'p2more17', 'ppac17', 'phisp17', 'racdiv17',
       'age5_17', 'age18_17', 'age24_17', 'age64_17', 'age65_17', 'hhs10',
       'femhhs17', 'fam17', 'hhsize10', 'mhhi17', 'hh25inc17', 'hh40inc17',
       'hh60inc17', 'hh75inc17', 'hhm7517', 'hhpov17', 'hhchpov17',
       'Shape__Area', 'Shape__Length', 'geometry', 'pointsinpolygon'],
      dtype='object')right_points_in_poly.head()right_points_in_poly.plot('pointsinpolygon', legend=True)<matplotlib.axes._subplots.AxesSubplot at 0x7fca55083ac8><Figure size 432x288 with 2 Axes>import altair as alt
import geopandas as gpd
import gpdvega
# GeoDataFrame could be passed as usual pd.DataFrame
chart = alt.Chart(right_points_in_poly).mark_geoshape(
).project(
).encode(
    color='pointsinpolygon', # shorthand infer types as for regular pd.DataFrame
    tooltip=['CSA2010','pointsinpolygon'] # GeoDataFrame.index is accessible as id
).properties(
    width=500,
    height=300
) 

routes = alt.Chart(gdf).mark_geoshape(
    filled=False,
    strokeWidth=2
)

chart + routes

chart.save(fileName[:-8]+'.html', embed_options={'renderer':'svg'})
chart.save(fileName[:-8]+'.json')
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0.387096774193548, "trip_count_percent": "0.0%", "trip_count_sum": 12, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.642785, 39.283256], [-76.642075, 39.283294]]}}, {"color": "rgb(205, 222, 236)", "id": 150159710, "streetname": "", "trip_count_average": 1.51612903225806, "trip_count_percent": "0.1%", "trip_count_sum": 47, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.579194, 39.280414], [-76.579178, 39.280235]]}}, {"color": "rgb(194, 214, 232)", "id": 150156733, "streetname": "", "trip_count_average": 2.35483870967742, "trip_count_percent": "0.1%", "trip_count_sum": 73, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.615014, 39.286567], [-76.615006, 39.286544]]}}, {"color": "rgb(217, 231, 241)", "id": 150198087, "streetname": "East Madison Street", "trip_count_average": 0.806451612903226, "trip_count_percent": "0.0%", "trip_count_sum": 25, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.574753, 39.300209], [-76.574555, 39.300216], [-76.573906, 39.300239]]}}, {"color": "rgb(212, 227, 239)", "id": 150153554, "streetname": "Emory Street", "trip_count_average": 1.09677419354839, "trip_count_percent": "0.0%", "trip_count_sum": 34, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.624718, 39.286167], [-76.624703, 39.285686]]}}, {"color": "rgb(192, 212, 231)", "id": 150217367, "streetname": "Perring Parkway", "trip_count_average": 2.58064516129032, "trip_count_percent": "0.1%", "trip_count_sum": 80, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.582177, 39.35028], [-76.581772, 39.350574], [-76.581437, 39.350841]]}}, {"color": "rgb(219, 232, 242)", "id": 150191249, "streetname": "East Chase Street", "trip_count_average": 0.741935483870968, "trip_count_percent": "0.0%", "trip_count_sum": 23, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.607644, 39.302707], [-76.607293, 39.302719]]}}, {"color": "rgb(211, 226, 238)", "id": 150188138, "streetname": "North Frederick Street", "trip_count_average": 1.12903225806452, "trip_count_percent": "0.0%", "trip_count_sum": 35, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.607789, 39.291355], [-76.607728, 39.290618]]}}, {"color": "rgb(194, 213, 231)", "id": 149970366, "streetname": "Reisterstown Road", "trip_count_average": 2.41935483870968, "trip_count_percent": "0.1%", "trip_count_sum": 75, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.700181, 39.354648], [-76.699799, 39.354381]]}}, {"color": "rgb(220, 233, 242)", "id": 150217175, "streetname": "", "trip_count_average": 0.67741935483871, "trip_count_percent": "0.0%", "trip_count_sum": 21, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.582444, 39.344722], [-76.582306, 39.344669], [-76.582131, 39.344589], [-76.581864, 39.34447]]}}, {"color": "rgb(143, 147, 197)", "id": 150156210, "streetname": "", "trip_count_average": 17.2903225806452, "trip_count_percent": "0.7%", "trip_count_sum": 536, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.610001, 39.28083], [-76.609719, 39.280834]]}}, {"color": "rgb(224, 236, 244)", "id": 150184120, "streetname": "North Caroline Street", "trip_count_average": 0.516129032258065, "trip_count_percent": "0.0%", "trip_count_sum": 16, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.59797, 39.304157], [-76.597901, 39.303077]]}}, {"color": "rgb(168, 194, 221)", "id": 150196766, "streetname": "", "trip_count_average": 5.80645161290323, "trip_count_percent": "0.2%", "trip_count_sum": 180, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.618561, 39.327087], [-76.61847, 39.327171], [-76.618432, 39.327201]]}}, {"color": "rgb(169, 194, 221)", "id": 150153354, "streetname": "MD 295 North", "trip_count_average": 5.64516129032258, "trip_count_percent": "0.2%", "trip_count_sum": 175, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.623421, 39.280483], [-76.623353, 39.280635]]}}, {"color": "rgb(152, 172, 210)", "id": 150156452, "streetname": "", "trip_count_average": 10.5806451612903, "trip_count_percent": "0.4%", "trip_count_sum": 328, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.617798, 39.284133], [-76.617806, 39.284065], [-76.617783, 39.283969]]}}, {"color": "rgb(178, 202, 225)", "id": 150162339, "streetname": "Hudson Street", "trip_count_average": 4.2258064516129, "trip_count_percent": "0.2%", "trip_count_sum": 131, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.567833, 39.282516], [-76.567696, 39.28252], [-76.567444, 39.282531]]}}, {"color": "rgb(186, 208, 229)", "id": 150159328, "streetname": "South Washington Street", "trip_count_average": 3.12903225806452, "trip_count_percent": "0.1%", "trip_count_sum": 97, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.58873, 39.284759], [-76.5887, 39.284313]]}}, {"color": "rgb(188, 209, 229)", "id": 150159833, "streetname": "", "trip_count_average": 2.93548387096774, "trip_count_percent": "0.1%", "trip_count_sum": 91, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.581383, 39.281337], [-76.581307, 39.281444]]}}, {"color": "rgb(225, 236, 244)", "id": 150201332, "streetname": "East Preston Street", "trip_count_average": 0.483870967741935, "trip_count_percent": "0.0%", "trip_count_sum": 15, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.582711, 39.305812], [-76.582634, 39.305816], [-76.582215, 39.305831]]}}, {"color": "rgb(186, 208, 229)", "id": 150188519, "streetname": "East Saratoga Street", "trip_count_average": 3.12903225806452, "trip_count_percent": "0.1%", "trip_count_sum": 97, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.615258, 39.292289], [-76.614899, 39.292301]]}}, {"color": "rgb(217, 231, 241)", "id": 149974514, "streetname": "Clipper Park Road", "trip_count_average": 0.806451612903226, "trip_count_percent": "0.0%", "trip_count_sum": 25, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.648209, 39.331081], [-76.647767, 39.331172]]}}, {"color": "rgb(140, 126, 186)", "id": 150195291, "streetname": "Maryland Avenue", "trip_count_average": 24.2903225806452, "trip_count_percent": "1.0%", "trip_count_sum": 753, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.618042, 39.312675], [-76.61802, 39.312164]]}}, {"color": "rgb(232, 242, 247)", "id": 150199324, "streetname": "", "trip_count_average": 0.258064516129032, "trip_count_percent": "0.0%", "trip_count_sum": 8, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.578919, 39.292121], [-76.578911, 39.292041]]}}, {"color": "rgb(207, 223, 237)", "id": 150187820, "streetname": "Dolphin Street", "trip_count_average": 1.35483870967742, "trip_count_percent": "0.1%", "trip_count_sum": 42, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.62262, 39.304103], [-76.622376, 39.304141]]}}, {"color": "rgb(139, 105, 176)", "id": 150155614, "streetname": "", "trip_count_average": 32.4193548387097, "trip_count_percent": "1.3%", "trip_count_sum": 1005, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.610665, 39.280818], [-76.610589, 39.280807], [-76.610245, 39.280803], [-76.6101, 39.280803]]}}, {"color": "rgb(148, 164, 205)", "id": 150188243, "streetname": "", "trip_count_average": 12.6129032258065, "trip_count_percent": "0.5%", "trip_count_sum": 391, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.617661, 39.289268], [-76.617661, 39.289337]]}}, {"color": "rgb(211, 226, 238)", "id": 150159702, "streetname": "South Duncan Street", "trip_count_average": 1.12903225806452, "trip_count_percent": "0.0%", "trip_count_sum": 35, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.586487, 39.286186], [-76.586487, 39.286254]]}}, {"color": "rgb(194, 214, 232)", "id": 150190292, "streetname": "East Madison Street", "trip_count_average": 2.35483870967742, "trip_count_percent": "0.1%", "trip_count_sum": 73, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.612374, 39.298721], [-76.61155, 39.298744]]}}, {"color": "rgb(173, 197, 223)", "id": 150153588, "streetname": "", "trip_count_average": 5.0, "trip_count_percent": "0.2%", "trip_count_sum": 155, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.624459, 39.286258], [-76.624405, 39.286262], [-76.623932, 39.286277], [-76.62362, 39.286289]]}}]}}, {"mode": "vega-lite"});
</script><h2>Inspect Origins-Destinations Data</h2>
ls 'Trip origins-destinations by month'# @title Example form fields
#@markdown Forms support many types of fields.
fileName = 'Trip Destinations by block August 2019.geojson'  #@param ['Trip Destinations by block August 2019.geojson', 'Trip Destinations by block December 2019.geojson', 'Trip Destinations by block November 2019.geojson', 'Trip Destinations by block October 2019.geojson', 'Trip Destinations by block September 2019.geojson', 'Trip Origins by block August 2019.geojson', 'Trip Origins by block December 2019.geojson', 'Trip Origins by block November 2019.geojson', 'Trip Origins by block October 2019.geojson', 'Trip Origins by block September 2019.geojson']
columnName = "name"  #@param ['name', 'value', 'color', 'radius']

#@markdown ---

gdf = gpd.read_file( findFile('./', fileName) )

gdf.plot( column = columnName)
gdf.columns
gdf[['id','name', 'value', 'color', 'radius']].head(5)dxp.bar(x='color', y='value', data=gdf, aggfunc='median')dxp.scatter(x = "color", y = "value", data = gdf, aggfunc = "median")#hide
!pip install nbdev
from nbdev import *
! nbdev_upgrade<h2>Introduction</h2>
<p><a href="https://mybinder.org/v2/gh/karpatic/datalab/master?filepath=%2Fnotebooks%2F02_scooterExploration.ipynb"><img src="https://mybinder.org/badge_logo.svg" alt="Binder" /></a>
<a href="https://colab.research.google.com/github/karpatic/datalab/blob/master/notebooks/02_scooterExploration.ipynb"><img src="https://pete88b.github.io/fastpages/assets/badges/colab.svg" alt="Binder" /></a>
<a href="https://github.com/karpatic/datalab/tree/master/notebooks/02_scooterExploration.ipynb"><img src="https://pete88b.github.io/fastpages/assets/badges/github.svg" alt="Binder" /></a>
<a href="https://github.com/ellerbrock/open-source-badges/"><img src="https://badges.frapsoft.com/os/v3/open-source.svg?v=103" alt="Open Source Love svg3" /></a></p>
<p><a href="https://github.com/karpatic/datalabs/blob/master/LICENSE"><img src="https://img.shields.io/npm/l/all-contributors.svg?style=flat" alt="NPM License" /></a>
<a href="https://karpatic.github.io"><img src="http://img.shields.io/badge/Status-Active-green.svg" alt="Active" /></a>
<a href="https://pypi.python.org/pypi/dataplay/"><img src="https://img.shields.io/pypi/pyversions/dataplay.svg" alt="Python Versions" /></a>
<a href=""><img src="https://img.shields.io/github/last-commit/karpatic/datalabs.svg?style=flat" alt="GitHub last commit" /></a>
<a href="http://unmaintained.tech/"><img src="http://unmaintained.tech/badge.svg" alt="No Maintenance Intended" /></a> </p>
<p><a href="https://github.com/karpatic/datalabs"><img src="https://img.shields.io/github/stars/karpatic/datalabs.svg?style=social&amp;label=Star" alt="GitHub stars" /></a>
<a href="https://github.com/karpatic/datalabs"><img src="https://img.shields.io/github/watchers/karpatic/datalabs.svg?style=social&amp;label=Watch" alt="GitHub watchers" /></a>
<a href="https://github.com/karpatic/datalabs"><img src="https://img.shields.io/github/forks/karpatic/datalabs.svg?style=social&amp;label=Fork" alt="GitHub forks" /></a>
<a href="https://github.com/karpatic/datalabs"><img src="https://img.shields.io/github/followers/karpatic.svg?style=social&amp;label=Follow" alt="GitHub followers" /></a> </p>
<p><a href="https://twitter.com/intent/tweet?text=Check%20out%20this%20%E2%9C%A8%20colab%20by%20@bniajfi%20https://github.com/karpatic/datalabs%20%F0%9F%A4%97"><img src="https://img.shields.io/twitter/url/https/github.com/karpatic/datalabs.svg?style=social" alt="Tweet" /></a>
<a href="https://twitter.com/bniajfi"><img src="https://img.shields.io/twitter/follow/bniajfi.svg?style=social" alt="Twitter Follow" /></a></p>
<p>This chapter has brought to you in collaboration by Brian Kelly, Michael Vandi, Logan Shertz, and Charles Karpati.</p>
<p><strong>Dataset:</strong>
Scooter data: </p>
<ul>
<li>Routes: 3 months (September to August 2019)</li>
<li>Deployment/</li>
<li>Routes</li>
<li>Trip origins-destinations by month</li>
</ul>
# do you see this? - Carlos# Hello world! - Michael# Checking - Brian<h2>Local File Access (Optional)</h2>
# hide_output
# (Optional) Run this cell to gain access to Google Drive (Colabs only) 
from google.colab import drive

# Colabs operates in a virtualized enviornment
# Colabs default directory is at ~/content.
# We mount Drive into a temporary folder at '~/content/drive' 

drive.mount('/content/drive')Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly&response_type=code

Enter your authorization code:
··········
Mounted at /content/drive
<p>File path's will vary</p>
# cd ./drive/'My Drive'/BNIA/responsive_records/Routes# cd ../content/drive/My Drive/DATA/scooter/content/drive/My Drive/DATA/scooter
# Michael's Directory
# cd ./drive/'My Drive'/BNIA/'Scooter Use Data'/BNIAcd ./Routes/content/drive/My Drive/DATA/scooter/Routes
# hide_output
! ls leftRouts.csv			 'Routing November 2019.geojson'
 rightRouts.csv			 'Routing October 2019.geojson'
'Routing August 2019.geojson'	 'Routing September 2019.geojson'
'Routing December 2019.geojson'
# hide_output
!cd ../ && ls boundsdf.csv	 rightRouts.csv  'Trip origins-destinations by month'
 Deployment	 Routes
 leftRouts.csv	 scooterdf.csv
<h1><strong>Installs</strong></h1>
! pip install dexplot
!pip install folium
!pip install geopandas
!pip install ipyleaflet
!pip install gpdvega
!pip install dataplay<h1><strong>Imports</strong></h1>
import os
import pandas as pd
import geopandas as gpd
import dexplot as dxp
import folium as fol
import json
import altair as alt
import gpdvega
import dataplay
# These imports will handle everything
import os
import sys
import csv
from IPython.display import clear_output
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import geopandas as gpd
from geopandas import GeoDataFrame
import psycopg2
import pyproj
from pyproj import Proj, transform
# conda install -c conda-forge proj4
from shapely.geometry import Point
from shapely import wkb
from shapely.wkt import loads
# https://pypi.org/project/geopy/
from geopy.geocoders import Nominatim
import folium
from folium import plugins

from dataplay.merge import mergeDatasets 

import dexplot as dxp

# In case file is KML, enable support
import fiona
fiona.drvsupport.supported_drivers['kml'] = 'rw'
fiona.drvsupport.supported_drivers['KML'] = 'rw'

#this cell is good to copy
from shapely.geometry import LineString
pd.plotting.register_matplotlib_converters()

from dataplay.geoms import readInGeometryData
from shapely import wkt
from dataplay.geoms import readInGeometryData

import ipywidgets as widgets
!jupyter nbextension enable --py widgetsnbextension
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = 'all'
import ipywidgets as widgets
from ipywidgets import interact, interact_manual
alt.data_transformers.enable('default', max_rows=None)<h3>Convenience Functions</h3>
# Return the boundaries of a Linestring 
def split(geom):
  print( str( type(geom)) )
  #Linestring might not be the actual type, so this may need to be fied. 
  #IF its not a linestring then dont run it and stuff 'false' and the datatype
  if str( type(geom)) == "<class 'shapely.geometry.linestring.LineString'>" and not str(geom.boundary) == 'MULTIPOINT EMPTY':
    print(geom.boundary)
    left, right = geom.boundary
    print('left x: ', type(left.x), left.x)
    print('left y: ', type(left.y), left.y)
    print('right x: ', type(right.x), right.x)
    print('right y: ', type(right.y), right.y)
    return left.x, left.y, right.x, right.y
  else: return False, type(geom), False, type(geom)# To 'import' a script you wrote, map its filepath into the sys
def getPointsInPolygons(pts, polygons, ptsCoordCol, polygonsCoordCol): 
    count = 0 
    total = pts.size / len(pts.columns)
    # We're going to keep a list of how many points we find.
    pts_in_polys = []

    # Loop over polygons with index i.
    for i, poly in polygons.iterrows():
        # print('Searching for point within Geom:', poly )
        # Keep a list of points in this poly
        pts_in_this_poly = []

        # Now loop over all points with index j.
        for j, pt in pts.iterrows():
          if poly[polygonsCoordCol].contains(pt[ptsCoordCol]):
            # Then it's a hit! Add it to the list,
            # and drop it so we have less hunting.
            pts_in_this_poly.append(pt[ptsCoordCol])
            pts = pts.drop([j])

        # We could do all sorts, like grab a property of the
        # points, but let's just append the number of them.
        pts_in_polys.append(len(pts_in_this_poly))
        print('Found this many points within the Geom:', len(pts_in_this_poly) ) 
        count = count + len(pts_in_this_poly) 
        clear_output(wait=True)

    # Add the number of points for each poly to the dataframe.
    polygons['pointsinpolygon'] = gpd.GeoSeries(pts_in_polys)
    print( 'Total Points: ', total )
    print( 'Total Points in Polygons: ', count )
    print( 'Prcnt Points in Polygons: ', count / total )
    return polygons<h3>File Access Convenience Functions</h3>
# Find Relative Path to Files
def findFile(root, file):
    for d, subD, f in os.walk(root):
        if file in f:
            return "{1}/{0}".format(file, d)
            break 

# To 'import' a script you wrote, map its filepath into the sys
def addPath(root, file): sys.path.append(os.path.abspath( findFile( './', file) ))findFile('./', 'Routing September 2019.geojson')'.//Routing September 2019.geojson'ls leftRouts.csv                   'Routing November 2019.geojson'
 rightRouts.csv                  'Routing October 2019.geojson'
'Routing August 2019.geojson'    'Routing September 2019.geojson'
'Routing December 2019.geojson'
<h2>Inspect Deployment Data</h2>
ls 'Trip origins-destinations by month''Trip Destinations by block August 2019.geojson'
'Trip Destinations by block December 2019.geojson'
'Trip Destinations by block November 2019.geojson'
'Trip Destinations by block October 2019.geojson'
'Trip Destinations by block September 2019.geojson'
'Trip Origins by block August 2019.geojson'
'Trip Origins by block December 2019.geojson'
'Trip Origins by block November 2019.geojson'
'Trip Origins by block October 2019.geojson'
'Trip Origins by block September 2019.geojson'
#@title Example form fields
#@markdown Forms support many types of fields.
fileName = "Trip Origins by block September 2019.geojson"  #@param ['Daily Deployment average by block August 2019.csv', 'Daily Deployment average by block December 2019.csv', 'Daily Deployment average by block November 2019.csv', 'Daily Deployment average by block October 2019.csv', 'Daily Deployment average by block September 2019.csv', 'Trip Destinations by block August 2019.geojson','Trip Destinations by block December 2019.geojson','Trip Destinations by block November 2019.geojson', 'Trip Destinations by block October 2019.geojson', 'Trip Destinations by block September 2019.geojson', 'Trip Origins by block August 2019.geojson','Trip Origins by block December 2019.geojson','Trip Origins by block November 2019.geojson','Trip Origins by block October 2019.geojson','Trip Origins by block September 2019.geojson']
#@markdown ---gdf = gpd.read_file( findFile('./', fileName) )
gdf.head()<div>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>id</th>
      <th>name</th>
      <th>color</th>
      <th>radius</th>
      <th>value</th>
      <th>geometry</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>1</td>
      <td>Block 245102502063018</td>
      <td>#fff</td>
      <td>4.999286</td>
      <td>NaN</td>
      <td>POLYGON ((-76.66168 39.26342, -76.66136 39.26373, -76.66096 39.26343, -76.66041 39.26306, -76.66053 39.26296, -76.66070 39.26281, -76.66077 39.26284, -76.66100 39.26296, -76.66117 39.26306, -76.66129 39.26315, -76.66141 39.26322, -76.66168 39.26342))</td>
    </tr>
    <tr>
      <th>1</th>
      <td>2</td>
      <td>Block 245102006001022</td>
      <td>#fff</td>
      <td>4.999286</td>
      <td>NaN</td>
      <td>POLYGON ((-76.66319 39.28427, -76.66306 39.28436, -76.66301 39.28439, -76.66293 39.28439, -76.66271 39.28418, -76.66303 39.28402, -76.66317 39.28417, -76.66320 39.28423, -76.66319 39.28427))</td>
    </tr>
    <tr>
      <th>2</th>
      <td>3</td>
      <td>Block 245102804033017</td>
      <td>#fff</td>
      <td>4.999286</td>
      <td>NaN</td>
      <td>POLYGON ((-76.71122 39.27927, -76.71121 39.27975, -76.71121 39.27990, -76.71119 39.28076, -76.71045 39.27932, -76.71045 39.27920, -76.71056 39.27907, -76.71123 39.27880, -76.71122 39.27927))</td>
    </tr>
    <tr>
      <th>3</th>
      <td>4</td>
      <td>Block 245102716006003</td>
      <td>#fff</td>
      <td>4.999286</td>
      <td>NaN</td>
      <td>POLYGON ((-76.66960 39.34512, -76.66905 39.34526, -76.66886 39.34531, -76.66848 39.34539, -76.66835 39.34502, -76.66867 39.34498, -76.66924 39.34484, -76.66946 39.34475, -76.66960 39.34512))</td>
    </tr>
    <tr>
      <th>4</th>
      <td>5</td>
      <td>Block 245102709023013</td>
      <td>#fff</td>
      <td>4.999286</td>
      <td>NaN</td>
      <td>POLYGON ((-76.58976 39.35401, -76.58972 39.35415, -76.58966 39.35435, -76.58964 39.35447, -76.58960 39.35469, -76.58940 39.35467, -76.58925 39.35465, -76.58912 39.35465, -76.58902 39.35464, -76.58898 39.35464, -76.58906 39.35425, -76.58917 39.35385, -76.58925 39.35385, -76.58939 39.35386, -76.58979 39.35393, -76.58976 39.35401))</td>
    </tr>
  </tbody>
</table>
</div>gdf.plot(column = 'value')<matplotlib.axes._subplots.AxesSubplot at 0x7f2c97160da0><Figure size 432x288 with 1 Axes>point_df = gdf.copy()
point_df['centroids'] = df.centroid
point_df = point_df.drop(columns = 'geometry')
point_df = point_df.set_geometry('centroids')
point_df.head(1)
point_df.plot(marker='o', color='red', markersize='value')point_df.value.hist()point_df[point_df.value > 1000].plot()baltimore = gpd.read_file("https://opendata.arcgis.com/datasets/b738a8587b6d479a8824d937892701d8_0.geojson")from dataplay.geoms import workWithGeometryDatabaltimore.columnsIndex(['OBJECTID', 'CSA2010', 'tpop10', 'male10', 'female10', 'paa17',
       'pwhite17', 'pasi17', 'p2more17', 'ppac17', 'phisp17', 'racdiv17',
       'age5_17', 'age18_17', 'age24_17', 'age64_17', 'age65_17', 'hhs10',
       'femhhs17', 'fam17', 'hhsize10', 'mhhi17', 'hh25inc17', 'hh40inc17',
       'hh60inc17', 'hh75inc17', 'hhm7517', 'hhpov17', 'hhchpov17',
       'Shape__Area', 'Shape__Length', 'geometry'],
      dtype='object')pointsInPolys = workWithGeometryData(method = 'ponp', df = point_df, polys = baltimore, ptsCoordCol ='centroids', 
                                     polygonsCoordCol = 'geometry', polygonsLabel = 'CSA2010')
pointsInPolys.plot(column='value', legend=True, markersize = 1)Total Points:  13506.0
Total Points in Polygons:  13453
Prcnt Points in Polygons:  0.9960758181548941
<matplotlib.axes._subplots.AxesSubplot at 0x7ff7c35c92e8><Figure size 432x288 with 2 Axes>ponp_copy = pointsInPolys.copy()ponp_copy.value.isnull().groupby([ponp_copy['CSA2010']]).sum().astype(int).reset_index(name='NumberMissingCount').head()<div>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>CSA2010</th>
      <th>NumberMissingCount</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>Allendale/Irvington/S. Hilton</td>
      <td>290</td>
    </tr>
    <tr>
      <th>1</th>
      <td>Beechfield/Ten Hills/West Hills</td>
      <td>180</td>
    </tr>
    <tr>
      <th>2</th>
      <td>Belair-Edison</td>
      <td>250</td>
    </tr>
    <tr>
      <th>3</th>
      <td>Brooklyn/Curtis Bay/Hawkins Point</td>
      <td>580</td>
    </tr>
    <tr>
      <th>4</th>
      <td>Canton</td>
      <td>0</td>
    </tr>
  </tbody>
</table>
</div>ponp_copy.value.notnull().groupby([ponp_copy['CSA2010']]).sum().astype(int) / ponp_copy.value.isnull().groupby([ponp_copy['CSA2010']]).sum().astype(int)ponp_copy.value.isnull().groupby([ponp_copy['CSA2010']]).sum().astype(int) / ponp_copy.value.groupby([ponp_copy['CSA2010']]).sum().astype(int) * 100ponp_copy.fillna(-1).groupby('CSA2010')['value'].sum()gdf.value.unique()gdf.value.describe()count    5677.000000
mean     40.862603  
std      175.769408 
min      1.000000   
25%      2.000000   
50%      5.000000   
75%      21.000000  
max      6978.000000
Name: value, dtype: float64gdf.value.var() # Return unbiased variance over requested axis.30894.884924369213gdf.value.sem() # Return unbiased standard error of the mean over requested axis.2.332834040372481gdf.value.nunique()	# Count distinct observations over requested axis.364# DataFrame.shape	Return a tuple representing the dimensionality of the DataFrame.
gdf.shape

#DataFrame.size	Return an int representing the number of elements in this object.
gdf.size

# DataFrame.ndim	Return an integer representing the number of axes/array dimensions.
gdf.ndim

# Note Used : 
# DataFrame.axes	Return a list representing the axes of the DataFrame.

gdf.dtypes

# Return unbiased kurtosis over requested axis using Fisher’s definition of kurtosis (kurtosis of normal == 0.0).
gdf.kurtosis()(13506, 6)810362id          object  
name        object  
color       object  
radius      float64 
value       float64 
geometry    geometry
dtype: objectid       -1.200000   
radius    1132.851562
value     495.784213 
dtype: float64<h3>Load Tracts</h3>
# This will just beautify the output.

pd.set_option('display.expand_frame_repr', False)
pd.set_option('display.precision', 2)
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"

pd.set_option('max_colwidth', 20)# The attributes are what we will use.
in_crs = 2248 # The CRS we recieve our data.
out_crs = 4326 # The CRS we would like to have our data represented as.
geom = 'geometry' # The column where our spatial information lives.

# A Url to load
boundariesBaltimoreTractsNoWater2010 = "https://docs.google.com/spreadsheets/d/e/2PACX-1vQ8xXdUaT17jkdK0MWTJpg3GOy6jMWeaXTlguXNjCSb8Vr_FanSZQRaTU-m811fQz4kyMFK5wcahMNY/pub?gid=886223646&single=true&output=csv"

# Read in the dataframe
gdf = pd.read_csv(boundariesBaltimoreTractsNoWater2010)

# Convert the geometry column datatype from a string of text into a coordinate datatype.
gdf['geometry'] = gdf['geometry'].apply(lambda x: loads( str(x) ))

# Process the dataframe as a geodataframe with a known CRS and geom column.
gdf = GeoDataFrame(gdf, crs=in_crs, geometry='geometry')boundariesBaltimoreTractsNoWater2010.head()<p>Ensure merge is on consistent datatypes</p>
gdf['GEOID10'] = gdf['GEOID10'].astype(str)
scooterdf['nameChange2'] = scooterdf['nameChange2'].astype(str)<p>Perform the merge</p>
scooterdfClean = gdf.merge(scooterdf, left_on='GEOID10', right_on='nameChange2').drop(['name', 'nameChange1', 'nameChange2'], axis=1)scooterdfClean.head()# scooterdf.to_csv('./scooterdf.csv', index=False)
# gdf.drop(columns='geometry').to_csv('./boundsdf.csv', index=False)lsscooterdfClean.groupby('CSA')['value'].sum()scooterdfClean.value.isna().groupby([scooterdfClean['CSA']]).sum().astype(int).reset_index(name='notApplicable')scooterdfClean.value.notnull().groupby([scooterdfClean['CSA']]).sum().astype(int).reset_index(name='NotMissingCount')scooterdfClean.value.isnull().groupby([scooterdfClean['CSA']]).sum().astype(int).reset_index(name='NumberMissingCount')scooterdfClean.fillna(-1).groupby('CSA')['value'].sum()scooterdfClean.groupby('CSA')['value'].mean()scooterdfClean.groupby('CSA')['value'].sum()scooterdfClean.CSA.value_counts()https://pandas.pydata.org/docs/getting_started/intro_tutorials/06_calculate_statistics.html<h2>Inspect Routes Data</h2>
fileName = 'Routing December 2019.geojson'  #@param ['Routing August 2019.geojson', 'Routing October 2019.geojson', 'Routing December 2019.geojson', 'Routing September 2019.geojson', 'Routing November 2019.geojson']
columnName = "streetname"  #@param ['id', 'color', 'streetname', 'trip_count_sum', 'trip_count_average', 'trip_count_percent']
gdf = gpd.read_file( findFile('./', fileName) )
gdf.head()<div>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>id</th>
      <th>color</th>
      <th>streetname</th>
      <th>trip_count_sum</th>
      <th>trip_count_average</th>
      <th>trip_count_percent</th>
      <th>geometry</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>150197772</td>
      <td>rgb(218, 231, 241)</td>
      <td>Jefferson Street</td>
      <td>24</td>
      <td>0.774194</td>
      <td>0.0%</td>
      <td>LINESTRING (-76.58576 39.29660, -76.58550 39.29661)</td>
    </tr>
    <tr>
      <th>1</th>
      <td>150155955</td>
      <td>rgb(164, 190, 219)</td>
      <td></td>
      <td>206</td>
      <td>6.645161</td>
      <td>0.3%</td>
      <td>LINESTRING (-76.61320 39.28136, -76.61318 39.28115)</td>
    </tr>
    <tr>
      <th>2</th>
      <td>150191673</td>
      <td>rgb(204, 221, 236)</td>
      <td>Harford Avenue</td>
      <td>49</td>
      <td>1.580645</td>
      <td>0.1%</td>
      <td>LINESTRING (-76.60169 39.30535, -76.60163 39.30544, -76.60156 39.30555)</td>
    </tr>
    <tr>
      <th>3</th>
      <td>150184657</td>
      <td>rgb(229, 239, 246)</td>
      <td></td>
      <td>11</td>
      <td>0.354839</td>
      <td>0.0%</td>
      <td>LINESTRING (-76.61493 39.29294, -76.61491 39.29258, -76.61490 39.29230)</td>
    </tr>
    <tr>
      <th>4</th>
      <td>150188407</td>
      <td>rgb(169, 194, 221)</td>
      <td></td>
      <td>178</td>
      <td>5.741935</td>
      <td>0.2%</td>
      <td>LINESTRING (-76.61662 39.29053, -76.61661 39.29046)</td>
    </tr>
  </tbody>
</table>
</div>baltimore = gpd.read_file("https://opendata.arcgis.com/datasets/b738a8587b6d479a8824d937892701d8_0.geojson")tst = """background = alt.Chart(gdf).mark_geoshape(
    stroke='white',
    strokeWidth=2
).encode(
    color=alt.value('#eee'),
).properties(
    width=700,
    height=500
)"""

# GeoDataFrame could be passed as usual pd.DataFrame
city = alt.Chart(baltimore).mark_geoshape(
).project(
).encode(
    color='tpop10', # shorthand infer types as for regular pd.DataFrame
    tooltip='CSA2010' # GeoDataFrame.index is accessible as id
).properties(
    width=500,
    height=300
)

routes = alt.Chart(gdf).mark_geoshape(
    filled=False,
    strokeWidth=2
)

city + routes<p>Clean the gdf of empties.</p>
gdf = gdf[~gdf.isna()]
gdf = gdf[~gdf.is_empty]<p>Now get the extremities; this will take a minute.</p>
# hide_output
gdf['lefty'], gdf['leftx'],gdf['righty'], gdf['rightx'] = zip(*gdf["geometry"].map(split))<p>Split the gdf into a left and right dataset</p>
gdf_left = gdf.copy()
gdf_left = gdf_left.drop(columns = ['geometry','rightx', 'righty'])

gdf_right= gdf.copy()
gdf_right = gdf_right.drop(columns = ['geometry','leftx', 'lefty', 'streetname', 'trip_count_sum', 'trip_count_average', 'trip_count_percent', 'color' ])<p>The coordinate variables of object type will cause problems.</p>
gdf_right.dtypesid        int64  
righty    float64
rightx    object 
dtype: object<p>Let's go ahead and coerce a correction.</p>
gdf_right['righty']=pd.to_numeric(gdf_right['righty'], errors='coerce')
gdf_left['lefty']=pd.to_numeric(gdf_left['lefty'], errors='coerce')

gdf_right['rightx']=pd.to_numeric(gdf_right['rightx'], errors='coerce')
gdf_left['leftx']=pd.to_numeric(gdf_left['leftx'], errors='coerce')<p>Now we can view the results</p>
gdf_right.dtypesid        int64  
righty    float64
rightx    float64
dtype: object<p>Save these csv's because it took a minute to get to where we are now.</p>
gdf_right.to_csv('rightRouts.csv')
gdf_left.to_csv('leftRouts.csv')<p>Convert the datasets to geodataframes for further exploration!</p>
# We could create a geodataframe like this
#temp_gdf = gpd.GeoDataFrame( gdf_right, geometry=gpd.points_from_xy(gdf_right.rightx, gdf_right.righty) )
#temp_gdf.head()

# Alternately this could work.. unfinished.. but wkt.loads can make a Point from text
# gdf_right['strCol']=gdf_right['rightx'].astype(str)
# gdf_right['geometry'] = gdf_right['strCol'].apply(wkt.loads)# hide_output
left_csaMap = readInGeometryData(url=gdf_left, porg='p', geom= False, lat= 'leftx', lng= 'lefty', revgeocode=False, save=False, in_crs=4268, out_crs=4268)# hide_output
right_csaMap = readInGeometryData(url=gdf_right, porg='p', geom= False, lat= 'rightx', lng= 'righty', revgeocode=False, save=False, in_crs=4268, out_crs=4268)right_csaMap.head()<div>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>id</th>
      <th>righty</th>
      <th>rightx</th>
      <th>geometry</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>150197772</td>
      <td>-76.585503</td>
      <td>39.296607</td>
      <td>POINT (-76.58550 39.29661)</td>
    </tr>
    <tr>
      <th>1</th>
      <td>150155955</td>
      <td>-76.613183</td>
      <td>39.281147</td>
      <td>POINT (-76.61318 39.28115)</td>
    </tr>
    <tr>
      <th>2</th>
      <td>150191673</td>
      <td>-76.601555</td>
      <td>39.305545</td>
      <td>POINT (-76.60156 39.30555)</td>
    </tr>
    <tr>
      <th>3</th>
      <td>150184657</td>
      <td>-76.614899</td>
      <td>39.292301</td>
      <td>POINT (-76.61490 39.29230)</td>
    </tr>
    <tr>
      <th>4</th>
      <td>150188407</td>
      <td>-76.616608</td>
      <td>39.290458</td>
      <td>POINT (-76.61661 39.29046)</td>
    </tr>
  </tbody>
</table>
</div>left_csaMap.head()left_csaMap.plot(column='trip_count_sum')right_csaMap.plot()baltimore.columns# hide_output
right_points_in_poly = getPointsInPolygons(right_csaMap, baltimore, 'geometry', 'geometry')right_csaMap.head()left_points_in_poly = getPointsInPolygons(left_csaMap, baltimore, 'geometry', 'geometry')left_csaMap.head()left_points_in_poly.drop('geometry', axis=1)[['pointsinpolygon']].head(20)<div>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>pointsinpolygon</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>18</td>
    </tr>
    <tr>
      <th>1</th>
      <td>0</td>
    </tr>
    <tr>
      <th>2</th>
      <td>67</td>
    </tr>
    <tr>
      <th>3</th>
      <td>0</td>
    </tr>
    <tr>
      <th>4</th>
      <td>1536</td>
    </tr>
    <tr>
      <th>5</th>
      <td>18</td>
    </tr>
    <tr>
      <th>6</th>
      <td>32</td>
    </tr>
    <tr>
      <th>7</th>
      <td>8</td>
    </tr>
    <tr>
      <th>8</th>
      <td>1</td>
    </tr>
    <tr>
      <th>9</th>
      <td>65</td>
    </tr>
    <tr>
      <th>10</th>
      <td>0</td>
    </tr>
    <tr>
      <th>11</th>
      <td>0</td>
    </tr>
    <tr>
      <th>12</th>
      <td>13</td>
    </tr>
    <tr>
      <th>13</th>
      <td>1764</td>
    </tr>
    <tr>
      <th>14</th>
      <td>0</td>
    </tr>
    <tr>
      <th>15</th>
      <td>760</td>
    </tr>
    <tr>
      <th>16</th>
      <td>0</td>
    </tr>
    <tr>
      <th>17</th>
      <td>85</td>
    </tr>
    <tr>
      <th>18</th>
      <td>1253</td>
    </tr>
    <tr>
      <th>19</th>
      <td>2</td>
    </tr>
  </tbody>
</table>
</div>right_points_in_poly.drop('geometry', axis=1)[['pointsinpolygon']].head(20)<div>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>pointsinpolygon</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>18</td>
    </tr>
    <tr>
      <th>1</th>
      <td>0</td>
    </tr>
    <tr>
      <th>2</th>
      <td>67</td>
    </tr>
    <tr>
      <th>3</th>
      <td>0</td>
    </tr>
    <tr>
      <th>4</th>
      <td>1536</td>
    </tr>
    <tr>
      <th>5</th>
      <td>18</td>
    </tr>
    <tr>
      <th>6</th>
      <td>32</td>
    </tr>
    <tr>
      <th>7</th>
      <td>8</td>
    </tr>
    <tr>
      <th>8</th>
      <td>1</td>
    </tr>
    <tr>
      <th>9</th>
      <td>65</td>
    </tr>
    <tr>
      <th>10</th>
      <td>0</td>
    </tr>
    <tr>
      <th>11</th>
      <td>0</td>
    </tr>
    <tr>
      <th>12</th>
      <td>13</td>
    </tr>
    <tr>
      <th>13</th>
      <td>1764</td>
    </tr>
    <tr>
      <th>14</th>
      <td>0</td>
    </tr>
    <tr>
      <th>15</th>
      <td>760</td>
    </tr>
    <tr>
      <th>16</th>
      <td>0</td>
    </tr>
    <tr>
      <th>17</th>
      <td>85</td>
    </tr>
    <tr>
      <th>18</th>
      <td>1253</td>
    </tr>
    <tr>
      <th>19</th>
      <td>2</td>
    </tr>
  </tbody>
</table>
</div>left_points_in_poly.plot(column='pointsinpolygon', legend=True)<matplotlib.axes._subplots.AxesSubplot at 0x7fca535fbbe0><Figure size 432x288 with 2 Axes>right_points_in_poly.plot(column='pointsinpolygon', legend=True)<matplotlib.axes._subplots.AxesSubplot at 0x7fca51773978><Figure size 432x288 with 2 Axes>right_points_in_polyscooterdfClean = left_points_in_poly.merge(right_points_in_poly, left_on='CSA2010', right_on='CSA2010', how='left')scooterdfClean.columnsright_points_in_poly.columnsIndex(['OBJECTID', 'CSA2010', 'tpop10', 'male10', 'female10', 'paa17',
       'pwhite17', 'pasi17', 'p2more17', 'ppac17', 'phisp17', 'racdiv17',
       'age5_17', 'age18_17', 'age24_17', 'age64_17', 'age65_17', 'hhs10',
       'femhhs17', 'fam17', 'hhsize10', 'mhhi17', 'hh25inc17', 'hh40inc17',
       'hh60inc17', 'hh75inc17', 'hhm7517', 'hhpov17', 'hhchpov17',
       'Shape__Area', 'Shape__Length', 'geometry', 'pointsinpolygon'],
      dtype='object')right_points_in_poly.head()right_points_in_poly.plot('pointsinpolygon', legend=True)<matplotlib.axes._subplots.AxesSubplot at 0x7fca55083ac8><Figure size 432x288 with 2 Axes>import altair as alt
import geopandas as gpd
import gpdvega
# GeoDataFrame could be passed as usual pd.DataFrame
chart = alt.Chart(right_points_in_poly).mark_geoshape(
).project(
).encode(
    color='pointsinpolygon', # shorthand infer types as for regular pd.DataFrame
    tooltip=['CSA2010','pointsinpolygon'] # GeoDataFrame.index is accessible as id
).properties(
    width=500,
    height=300
) 

routes = alt.Chart(gdf).mark_geoshape(
    filled=False,
    strokeWidth=2
)

chart + routes

chart.save(fileName[:-8]+'.html', embed_options={'renderer':'svg'})
chart.save(fileName[:-8]+'.json')
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{"type": "LineString", "coordinates": [[-76.586487, 39.286186], [-76.586487, 39.286254]]}}, {"color": "rgb(194, 214, 232)", "id": 150190292, "streetname": "East Madison Street", "trip_count_average": 2.35483870967742, "trip_count_percent": "0.1%", "trip_count_sum": 73, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.612374, 39.298721], [-76.61155, 39.298744]]}}, {"color": "rgb(173, 197, 223)", "id": 150153588, "streetname": "", "trip_count_average": 5.0, "trip_count_percent": "0.2%", "trip_count_sum": 155, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.624459, 39.286258], [-76.624405, 39.286262], [-76.623932, 39.286277], [-76.62362, 39.286289]]}}]}}, {"mode": "vega-lite"});
</script><h2>Inspect Origins-Destinations Data</h2>
ls 'Trip origins-destinations by month'# @title Example form fields
#@markdown Forms support many types of fields.
fileName = 'Trip Destinations by block August 2019.geojson'  #@param ['Trip Destinations by block August 2019.geojson', 'Trip Destinations by block December 2019.geojson', 'Trip Destinations by block November 2019.geojson', 'Trip Destinations by block October 2019.geojson', 'Trip Destinations by block September 2019.geojson', 'Trip Origins by block August 2019.geojson', 'Trip Origins by block December 2019.geojson', 'Trip Origins by block November 2019.geojson', 'Trip Origins by block October 2019.geojson', 'Trip Origins by block September 2019.geojson']
columnName = "name"  #@param ['name', 'value', 'color', 'radius']

#@markdown ---

gdf = gpd.read_file( findFile('./', fileName) )

gdf.plot( column = columnName)
gdf.columns
gdf[['id','name', 'value', 'color', 'radius']].head(5)dxp.bar(x='color', y='value', data=gdf, aggfunc='median')dxp.scatter(x = "color", y = "value", data = gdf, aggfunc = "median")#hide
!pip install nbdev
from nbdev import *
! nbdev_upgrade<h2>Introduction</h2>
<p><a href="https://mybinder.org/v2/gh/karpatic/datalab/master?filepath=%2Fnotebooks%2F02_scooterExploration.ipynb"><img src="https://mybinder.org/badge_logo.svg" alt="Binder" /></a>
<a href="https://colab.research.google.com/github/karpatic/datalab/blob/master/notebooks/02_scooterExploration.ipynb"><img src="https://pete88b.github.io/fastpages/assets/badges/colab.svg" alt="Binder" /></a>
<a href="https://github.com/karpatic/datalab/tree/master/notebooks/02_scooterExploration.ipynb"><img src="https://pete88b.github.io/fastpages/assets/badges/github.svg" alt="Binder" /></a>
<a href="https://github.com/ellerbrock/open-source-badges/"><img src="https://badges.frapsoft.com/os/v3/open-source.svg?v=103" alt="Open Source Love svg3" /></a></p>
<p><a href="https://github.com/karpatic/datalabs/blob/master/LICENSE"><img src="https://img.shields.io/npm/l/all-contributors.svg?style=flat" alt="NPM License" /></a>
<a href="https://karpatic.github.io"><img src="http://img.shields.io/badge/Status-Active-green.svg" alt="Active" /></a>
<a href="https://pypi.python.org/pypi/dataplay/"><img src="https://img.shields.io/pypi/pyversions/dataplay.svg" alt="Python Versions" /></a>
<a href=""><img src="https://img.shields.io/github/last-commit/karpatic/datalabs.svg?style=flat" alt="GitHub last commit" /></a>
<a href="http://unmaintained.tech/"><img src="http://unmaintained.tech/badge.svg" alt="No Maintenance Intended" /></a> </p>
<p><a href="https://github.com/karpatic/datalabs"><img src="https://img.shields.io/github/stars/karpatic/datalabs.svg?style=social&amp;label=Star" alt="GitHub stars" /></a>
<a href="https://github.com/karpatic/datalabs"><img src="https://img.shields.io/github/watchers/karpatic/datalabs.svg?style=social&amp;label=Watch" alt="GitHub watchers" /></a>
<a href="https://github.com/karpatic/datalabs"><img src="https://img.shields.io/github/forks/karpatic/datalabs.svg?style=social&amp;label=Fork" alt="GitHub forks" /></a>
<a href="https://github.com/karpatic/datalabs"><img src="https://img.shields.io/github/followers/karpatic.svg?style=social&amp;label=Follow" alt="GitHub followers" /></a> </p>
<p><a href="https://twitter.com/intent/tweet?text=Check%20out%20this%20%E2%9C%A8%20colab%20by%20@bniajfi%20https://github.com/karpatic/datalabs%20%F0%9F%A4%97"><img src="https://img.shields.io/twitter/url/https/github.com/karpatic/datalabs.svg?style=social" alt="Tweet" /></a>
<a href="https://twitter.com/bniajfi"><img src="https://img.shields.io/twitter/follow/bniajfi.svg?style=social" alt="Twitter Follow" /></a></p>
<p>This chapter has brought to you in collaboration by Brian Kelly, Michael Vandi, Logan Shertz, and Charles Karpati.</p>
<p><strong>Dataset:</strong>
Scooter data: </p>
<ul>
<li>Routes: 3 months (September to August 2019)</li>
<li>Deployment/</li>
<li>Routes</li>
<li>Trip origins-destinations by month</li>
</ul>
# do you see this? - Carlos# Hello world! - Michael# Checking - Brian<h2>Local File Access (Optional)</h2>
# hide_output
# (Optional) Run this cell to gain access to Google Drive (Colabs only) 
from google.colab import drive

# Colabs operates in a virtualized enviornment
# Colabs default directory is at ~/content.
# We mount Drive into a temporary folder at '~/content/drive' 

drive.mount('/content/drive')Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly&response_type=code

Enter your authorization code:
··········
Mounted at /content/drive
<p>File path's will vary</p>
# cd ./drive/'My Drive'/BNIA/responsive_records/Routes# cd ../content/drive/My Drive/DATA/scooter/content/drive/My Drive/DATA/scooter
# Michael's Directory
# cd ./drive/'My Drive'/BNIA/'Scooter Use Data'/BNIAcd ./Routes/content/drive/My Drive/DATA/scooter/Routes
# hide_output
! ls leftRouts.csv			 'Routing November 2019.geojson'
 rightRouts.csv			 'Routing October 2019.geojson'
'Routing August 2019.geojson'	 'Routing September 2019.geojson'
'Routing December 2019.geojson'
# hide_output
!cd ../ && ls boundsdf.csv	 rightRouts.csv  'Trip origins-destinations by month'
 Deployment	 Routes
 leftRouts.csv	 scooterdf.csv
<h1><strong>Installs</strong></h1>
! pip install dexplot
!pip install folium
!pip install geopandas
!pip install ipyleaflet
!pip install gpdvega
!pip install dataplay<h1><strong>Imports</strong></h1>
import os
import pandas as pd
import geopandas as gpd
import dexplot as dxp
import folium as fol
import json
import altair as alt
import gpdvega
import dataplay
# These imports will handle everything
import os
import sys
import csv
from IPython.display import clear_output
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import geopandas as gpd
from geopandas import GeoDataFrame
import psycopg2
import pyproj
from pyproj import Proj, transform
# conda install -c conda-forge proj4
from shapely.geometry import Point
from shapely import wkb
from shapely.wkt import loads
# https://pypi.org/project/geopy/
from geopy.geocoders import Nominatim
import folium
from folium import plugins

from dataplay.merge import mergeDatasets 

import dexplot as dxp

# In case file is KML, enable support
import fiona
fiona.drvsupport.supported_drivers['kml'] = 'rw'
fiona.drvsupport.supported_drivers['KML'] = 'rw'

#this cell is good to copy
from shapely.geometry import LineString
pd.plotting.register_matplotlib_converters()

from dataplay.geoms import readInGeometryData
from shapely import wkt
from dataplay.geoms import readInGeometryData

import ipywidgets as widgets
!jupyter nbextension enable --py widgetsnbextension
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = 'all'
import ipywidgets as widgets
from ipywidgets import interact, interact_manual
alt.data_transformers.enable('default', max_rows=None)<h3>Convenience Functions</h3>
# Return the boundaries of a Linestring 
def split(geom):
  print( str( type(geom)) )
  #Linestring might not be the actual type, so this may need to be fied. 
  #IF its not a linestring then dont run it and stuff 'false' and the datatype
  if str( type(geom)) == "<class 'shapely.geometry.linestring.LineString'>" and not str(geom.boundary) == 'MULTIPOINT EMPTY':
    print(geom.boundary)
    left, right = geom.boundary
    print('left x: ', type(left.x), left.x)
    print('left y: ', type(left.y), left.y)
    print('right x: ', type(right.x), right.x)
    print('right y: ', type(right.y), right.y)
    return left.x, left.y, right.x, right.y
  else: return False, type(geom), False, type(geom)# To 'import' a script you wrote, map its filepath into the sys
def getPointsInPolygons(pts, polygons, ptsCoordCol, polygonsCoordCol): 
    count = 0 
    total = pts.size / len(pts.columns)
    # We're going to keep a list of how many points we find.
    pts_in_polys = []

    # Loop over polygons with index i.
    for i, poly in polygons.iterrows():
        # print('Searching for point within Geom:', poly )
        # Keep a list of points in this poly
        pts_in_this_poly = []

        # Now loop over all points with index j.
        for j, pt in pts.iterrows():
          if poly[polygonsCoordCol].contains(pt[ptsCoordCol]):
            # Then it's a hit! Add it to the list,
            # and drop it so we have less hunting.
            pts_in_this_poly.append(pt[ptsCoordCol])
            pts = pts.drop([j])

        # We could do all sorts, like grab a property of the
        # points, but let's just append the number of them.
        pts_in_polys.append(len(pts_in_this_poly))
        print('Found this many points within the Geom:', len(pts_in_this_poly) ) 
        count = count + len(pts_in_this_poly) 
        clear_output(wait=True)

    # Add the number of points for each poly to the dataframe.
    polygons['pointsinpolygon'] = gpd.GeoSeries(pts_in_polys)
    print( 'Total Points: ', total )
    print( 'Total Points in Polygons: ', count )
    print( 'Prcnt Points in Polygons: ', count / total )
    return polygons<h3>File Access Convenience Functions</h3>
# Find Relative Path to Files
def findFile(root, file):
    for d, subD, f in os.walk(root):
        if file in f:
            return "{1}/{0}".format(file, d)
            break 

# To 'import' a script you wrote, map its filepath into the sys
def addPath(root, file): sys.path.append(os.path.abspath( findFile( './', file) ))findFile('./', 'Routing September 2019.geojson')'.//Routing September 2019.geojson'ls leftRouts.csv                   'Routing November 2019.geojson'
 rightRouts.csv                  'Routing October 2019.geojson'
'Routing August 2019.geojson'    'Routing September 2019.geojson'
'Routing December 2019.geojson'
<h2>Inspect Deployment Data</h2>
ls 'Trip origins-destinations by month''Trip Destinations by block August 2019.geojson'
'Trip Destinations by block December 2019.geojson'
'Trip Destinations by block November 2019.geojson'
'Trip Destinations by block October 2019.geojson'
'Trip Destinations by block September 2019.geojson'
'Trip Origins by block August 2019.geojson'
'Trip Origins by block December 2019.geojson'
'Trip Origins by block November 2019.geojson'
'Trip Origins by block October 2019.geojson'
'Trip Origins by block September 2019.geojson'
#@title Example form fields
#@markdown Forms support many types of fields.
fileName = "Trip Origins by block September 2019.geojson"  #@param ['Daily Deployment average by block August 2019.csv', 'Daily Deployment average by block December 2019.csv', 'Daily Deployment average by block November 2019.csv', 'Daily Deployment average by block October 2019.csv', 'Daily Deployment average by block September 2019.csv', 'Trip Destinations by block August 2019.geojson','Trip Destinations by block December 2019.geojson','Trip Destinations by block November 2019.geojson', 'Trip Destinations by block October 2019.geojson', 'Trip Destinations by block September 2019.geojson', 'Trip Origins by block August 2019.geojson','Trip Origins by block December 2019.geojson','Trip Origins by block November 2019.geojson','Trip Origins by block October 2019.geojson','Trip Origins by block September 2019.geojson']
#@markdown ---gdf = gpd.read_file( findFile('./', fileName) )
gdf.head()<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>id</th>
      <th>name</th>
      <th>color</th>
      <th>radius</th>
      <th>value</th>
      <th>geometry</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>1</td>
      <td>Block 245102502063018</td>
      <td>#fff</td>
      <td>4.999286</td>
      <td>NaN</td>
      <td>POLYGON ((-76.66168 39.26342, -76.66136 39.26373, -76.66096 39.26343, -76.66041 39.26306, -76.66053 39.26296, -76.66070 39.26281, -76.66077 39.26284, -76.66100 39.26296, -76.66117 39.26306, -76.66129 39.26315, -76.66141 39.26322, -76.66168 39.26342))</td>
    </tr>
    <tr>
      <th>1</th>
      <td>2</td>
      <td>Block 245102006001022</td>
      <td>#fff</td>
      <td>4.999286</td>
      <td>NaN</td>
      <td>POLYGON ((-76.66319 39.28427, -76.66306 39.28436, -76.66301 39.28439, -76.66293 39.28439, -76.66271 39.28418, -76.66303 39.28402, -76.66317 39.28417, -76.66320 39.28423, -76.66319 39.28427))</td>
    </tr>
    <tr>
      <th>2</th>
      <td>3</td>
      <td>Block 245102804033017</td>
      <td>#fff</td>
      <td>4.999286</td>
      <td>NaN</td>
      <td>POLYGON ((-76.71122 39.27927, -76.71121 39.27975, -76.71121 39.27990, -76.71119 39.28076, -76.71045 39.27932, -76.71045 39.27920, -76.71056 39.27907, -76.71123 39.27880, -76.71122 39.27927))</td>
    </tr>
    <tr>
      <th>3</th>
      <td>4</td>
      <td>Block 245102716006003</td>
      <td>#fff</td>
      <td>4.999286</td>
      <td>NaN</td>
      <td>POLYGON ((-76.66960 39.34512, -76.66905 39.34526, -76.66886 39.34531, -76.66848 39.34539, -76.66835 39.34502, -76.66867 39.34498, -76.66924 39.34484, -76.66946 39.34475, -76.66960 39.34512))</td>
    </tr>
    <tr>
      <th>4</th>
      <td>5</td>
      <td>Block 245102709023013</td>
      <td>#fff</td>
      <td>4.999286</td>
      <td>NaN</td>
      <td>POLYGON ((-76.58976 39.35401, -76.58972 39.35415, -76.58966 39.35435, -76.58964 39.35447, -76.58960 39.35469, -76.58940 39.35467, -76.58925 39.35465, -76.58912 39.35465, -76.58902 39.35464, -76.58898 39.35464, -76.58906 39.35425, -76.58917 39.35385, -76.58925 39.35385, -76.58939 39.35386, -76.58979 39.35393, -76.58976 39.35401))</td>
    </tr>
  </tbody>
</table>
</div>gdf.plot(column = 'value')<matplotlib.axes._subplots.AxesSubplot at 0x7f2c97160da0><Figure size 432x288 with 1 Axes>point_df = gdf.copy()
point_df['centroids'] = df.centroid
point_df = point_df.drop(columns = 'geometry')
point_df = point_df.set_geometry('centroids')
point_df.head(1)
point_df.plot(marker='o', color='red', markersize='value')point_df.value.hist()point_df[point_df.value > 1000].plot()baltimore = gpd.read_file("https://opendata.arcgis.com/datasets/b738a8587b6d479a8824d937892701d8_0.geojson")from dataplay.geoms import workWithGeometryDatabaltimore.columnsIndex(['OBJECTID', 'CSA2010', 'tpop10', 'male10', 'female10', 'paa17',
       'pwhite17', 'pasi17', 'p2more17', 'ppac17', 'phisp17', 'racdiv17',
       'age5_17', 'age18_17', 'age24_17', 'age64_17', 'age65_17', 'hhs10',
       'femhhs17', 'fam17', 'hhsize10', 'mhhi17', 'hh25inc17', 'hh40inc17',
       'hh60inc17', 'hh75inc17', 'hhm7517', 'hhpov17', 'hhchpov17',
       'Shape__Area', 'Shape__Length', 'geometry'],
      dtype='object')pointsInPolys = workWithGeometryData(method = 'ponp', df = point_df, polys = baltimore, ptsCoordCol ='centroids', 
                                     polygonsCoordCol = 'geometry', polygonsLabel = 'CSA2010')
pointsInPolys.plot(column='value', legend=True, markersize = 1)Total Points:  13506.0
Total Points in Polygons:  13453
Prcnt Points in Polygons:  0.9960758181548941
<matplotlib.axes._subplots.AxesSubplot at 0x7ff7c35c92e8><Figure size 432x288 with 2 Axes>ponp_copy = pointsInPolys.copy()ponp_copy.value.isnull().groupby([ponp_copy['CSA2010']]).sum().astype(int).reset_index(name='NumberMissingCount').head()<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>CSA2010</th>
      <th>NumberMissingCount</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>Allendale/Irvington/S. Hilton</td>
      <td>290</td>
    </tr>
    <tr>
      <th>1</th>
      <td>Beechfield/Ten Hills/West Hills</td>
      <td>180</td>
    </tr>
    <tr>
      <th>2</th>
      <td>Belair-Edison</td>
      <td>250</td>
    </tr>
    <tr>
      <th>3</th>
      <td>Brooklyn/Curtis Bay/Hawkins Point</td>
      <td>580</td>
    </tr>
    <tr>
      <th>4</th>
      <td>Canton</td>
      <td>0</td>
    </tr>
  </tbody>
</table>
</div>ponp_copy.value.notnull().groupby([ponp_copy['CSA2010']]).sum().astype(int) / ponp_copy.value.isnull().groupby([ponp_copy['CSA2010']]).sum().astype(int)ponp_copy.value.isnull().groupby([ponp_copy['CSA2010']]).sum().astype(int) / ponp_copy.value.groupby([ponp_copy['CSA2010']]).sum().astype(int) * 100ponp_copy.fillna(-1).groupby('CSA2010')['value'].sum()gdf.value.unique()gdf.value.describe()count    5677.000000
mean     40.862603  
std      175.769408 
min      1.000000   
25%      2.000000   
50%      5.000000   
75%      21.000000  
max      6978.000000
Name: value, dtype: float64gdf.value.var() # Return unbiased variance over requested axis.30894.884924369213gdf.value.sem() # Return unbiased standard error of the mean over requested axis.2.332834040372481gdf.value.nunique()	# Count distinct observations over requested axis.364# DataFrame.shape	Return a tuple representing the dimensionality of the DataFrame.
gdf.shape

#DataFrame.size	Return an int representing the number of elements in this object.
gdf.size

# DataFrame.ndim	Return an integer representing the number of axes/array dimensions.
gdf.ndim

# Note Used : 
# DataFrame.axes	Return a list representing the axes of the DataFrame.

gdf.dtypes

# Return unbiased kurtosis over requested axis using Fisher’s definition of kurtosis (kurtosis of normal == 0.0).
gdf.kurtosis()(13506, 6)810362id          object  
name        object  
color       object  
radius      float64 
value       float64 
geometry    geometry
dtype: objectid       -1.200000   
radius    1132.851562
value     495.784213 
dtype: float64<h3>Load Tracts</h3>
# This will just beautify the output.

pd.set_option('display.expand_frame_repr', False)
pd.set_option('display.precision', 2)
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"

pd.set_option('max_colwidth', 20)# The attributes are what we will use.
in_crs = 2248 # The CRS we recieve our data.
out_crs = 4326 # The CRS we would like to have our data represented as.
geom = 'geometry' # The column where our spatial information lives.

# A Url to load
boundariesBaltimoreTractsNoWater2010 = "https://docs.google.com/spreadsheets/d/e/2PACX-1vQ8xXdUaT17jkdK0MWTJpg3GOy6jMWeaXTlguXNjCSb8Vr_FanSZQRaTU-m811fQz4kyMFK5wcahMNY/pub?gid=886223646&single=true&output=csv"

# Read in the dataframe
gdf = pd.read_csv(boundariesBaltimoreTractsNoWater2010)

# Convert the geometry column datatype from a string of text into a coordinate datatype.
gdf['geometry'] = gdf['geometry'].apply(lambda x: loads( str(x) ))

# Process the dataframe as a geodataframe with a known CRS and geom column.
gdf = GeoDataFrame(gdf, crs=in_crs, geometry='geometry')boundariesBaltimoreTractsNoWater2010.head()<p>Ensure merge is on consistent datatypes</p>
gdf['GEOID10'] = gdf['GEOID10'].astype(str)
scooterdf['nameChange2'] = scooterdf['nameChange2'].astype(str)<p>Perform the merge</p>
scooterdfClean = gdf.merge(scooterdf, left_on='GEOID10', right_on='nameChange2').drop(['name', 'nameChange1', 'nameChange2'], axis=1)scooterdfClean.head()# scooterdf.to_csv('./scooterdf.csv', index=False)
# gdf.drop(columns='geometry').to_csv('./boundsdf.csv', index=False)lsscooterdfClean.groupby('CSA')['value'].sum()scooterdfClean.value.isna().groupby([scooterdfClean['CSA']]).sum().astype(int).reset_index(name='notApplicable')scooterdfClean.value.notnull().groupby([scooterdfClean['CSA']]).sum().astype(int).reset_index(name='NotMissingCount')scooterdfClean.value.isnull().groupby([scooterdfClean['CSA']]).sum().astype(int).reset_index(name='NumberMissingCount')scooterdfClean.fillna(-1).groupby('CSA')['value'].sum()scooterdfClean.groupby('CSA')['value'].mean()scooterdfClean.groupby('CSA')['value'].sum()scooterdfClean.CSA.value_counts()https://pandas.pydata.org/docs/getting_started/intro_tutorials/06_calculate_statistics.html<h2>Inspect Routes Data</h2>
fileName = 'Routing December 2019.geojson'  #@param ['Routing August 2019.geojson', 'Routing October 2019.geojson', 'Routing December 2019.geojson', 'Routing September 2019.geojson', 'Routing November 2019.geojson']
columnName = "streetname"  #@param ['id', 'color', 'streetname', 'trip_count_sum', 'trip_count_average', 'trip_count_percent']
gdf = gpd.read_file( findFile('./', fileName) )
gdf.head()<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>id</th>
      <th>color</th>
      <th>streetname</th>
      <th>trip_count_sum</th>
      <th>trip_count_average</th>
      <th>trip_count_percent</th>
      <th>geometry</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>150197772</td>
      <td>rgb(218, 231, 241)</td>
      <td>Jefferson Street</td>
      <td>24</td>
      <td>0.774194</td>
      <td>0.0%</td>
      <td>LINESTRING (-76.58576 39.29660, -76.58550 39.29661)</td>
    </tr>
    <tr>
      <th>1</th>
      <td>150155955</td>
      <td>rgb(164, 190, 219)</td>
      <td></td>
      <td>206</td>
      <td>6.645161</td>
      <td>0.3%</td>
      <td>LINESTRING (-76.61320 39.28136, -76.61318 39.28115)</td>
    </tr>
    <tr>
      <th>2</th>
      <td>150191673</td>
      <td>rgb(204, 221, 236)</td>
      <td>Harford Avenue</td>
      <td>49</td>
      <td>1.580645</td>
      <td>0.1%</td>
      <td>LINESTRING (-76.60169 39.30535, -76.60163 39.30544, -76.60156 39.30555)</td>
    </tr>
    <tr>
      <th>3</th>
      <td>150184657</td>
      <td>rgb(229, 239, 246)</td>
      <td></td>
      <td>11</td>
      <td>0.354839</td>
      <td>0.0%</td>
      <td>LINESTRING (-76.61493 39.29294, -76.61491 39.29258, -76.61490 39.29230)</td>
    </tr>
    <tr>
      <th>4</th>
      <td>150188407</td>
      <td>rgb(169, 194, 221)</td>
      <td></td>
      <td>178</td>
      <td>5.741935</td>
      <td>0.2%</td>
      <td>LINESTRING (-76.61662 39.29053, -76.61661 39.29046)</td>
    </tr>
  </tbody>
</table>
</div>baltimore = gpd.read_file("https://opendata.arcgis.com/datasets/b738a8587b6d479a8824d937892701d8_0.geojson")tst = """background = alt.Chart(gdf).mark_geoshape(
    stroke='white',
    strokeWidth=2
).encode(
    color=alt.value('#eee'),
).properties(
    width=700,
    height=500
)"""

# GeoDataFrame could be passed as usual pd.DataFrame
city = alt.Chart(baltimore).mark_geoshape(
).project(
).encode(
    color='tpop10', # shorthand infer types as for regular pd.DataFrame
    tooltip='CSA2010' # GeoDataFrame.index is accessible as id
).properties(
    width=500,
    height=300
)

routes = alt.Chart(gdf).mark_geoshape(
    filled=False,
    strokeWidth=2
)

city + routes<p>Clean the gdf of empties.</p>
gdf = gdf[~gdf.isna()]
gdf = gdf[~gdf.is_empty]<p>Now get the extremities; this will take a minute.</p>
# hide_output
gdf['lefty'], gdf['leftx'],gdf['righty'], gdf['rightx'] = zip(*gdf["geometry"].map(split))<p>Split the gdf into a left and right dataset</p>
gdf_left = gdf.copy()
gdf_left = gdf_left.drop(columns = ['geometry','rightx', 'righty'])

gdf_right= gdf.copy()
gdf_right = gdf_right.drop(columns = ['geometry','leftx', 'lefty', 'streetname', 'trip_count_sum', 'trip_count_average', 'trip_count_percent', 'color' ])<p>The coordinate variables of object type will cause problems.</p>
gdf_right.dtypesid        int64  
righty    float64
rightx    object 
dtype: object<p>Let's go ahead and coerce a correction.</p>
gdf_right['righty']=pd.to_numeric(gdf_right['righty'], errors='coerce')
gdf_left['lefty']=pd.to_numeric(gdf_left['lefty'], errors='coerce')

gdf_right['rightx']=pd.to_numeric(gdf_right['rightx'], errors='coerce')
gdf_left['leftx']=pd.to_numeric(gdf_left['leftx'], errors='coerce')<p>Now we can view the results</p>
gdf_right.dtypesid        int64  
righty    float64
rightx    float64
dtype: object<p>Save these csv's because it took a minute to get to where we are now.</p>
gdf_right.to_csv('rightRouts.csv')
gdf_left.to_csv('leftRouts.csv')<p>Convert the datasets to geodataframes for further exploration!</p>
# We could create a geodataframe like this
#temp_gdf = gpd.GeoDataFrame( gdf_right, geometry=gpd.points_from_xy(gdf_right.rightx, gdf_right.righty) )
#temp_gdf.head()

# Alternately this could work.. unfinished.. but wkt.loads can make a Point from text
# gdf_right['strCol']=gdf_right['rightx'].astype(str)
# gdf_right['geometry'] = gdf_right['strCol'].apply(wkt.loads)# hide_output
left_csaMap = readInGeometryData(url=gdf_left, porg='p', geom= False, lat= 'leftx', lng= 'lefty', revgeocode=False, save=False, in_crs=4268, out_crs=4268)# hide_output
right_csaMap = readInGeometryData(url=gdf_right, porg='p', geom= False, lat= 'rightx', lng= 'righty', revgeocode=False, save=False, in_crs=4268, out_crs=4268)right_csaMap.head()<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>id</th>
      <th>righty</th>
      <th>rightx</th>
      <th>geometry</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>150197772</td>
      <td>-76.585503</td>
      <td>39.296607</td>
      <td>POINT (-76.58550 39.29661)</td>
    </tr>
    <tr>
      <th>1</th>
      <td>150155955</td>
      <td>-76.613183</td>
      <td>39.281147</td>
      <td>POINT (-76.61318 39.28115)</td>
    </tr>
    <tr>
      <th>2</th>
      <td>150191673</td>
      <td>-76.601555</td>
      <td>39.305545</td>
      <td>POINT (-76.60156 39.30555)</td>
    </tr>
    <tr>
      <th>3</th>
      <td>150184657</td>
      <td>-76.614899</td>
      <td>39.292301</td>
      <td>POINT (-76.61490 39.29230)</td>
    </tr>
    <tr>
      <th>4</th>
      <td>150188407</td>
      <td>-76.616608</td>
      <td>39.290458</td>
      <td>POINT (-76.61661 39.29046)</td>
    </tr>
  </tbody>
</table>
</div>left_csaMap.head()left_csaMap.plot(column='trip_count_sum')right_csaMap.plot()baltimore.columns# hide_output
right_points_in_poly = getPointsInPolygons(right_csaMap, baltimore, 'geometry', 'geometry')right_csaMap.head()left_points_in_poly = getPointsInPolygons(left_csaMap, baltimore, 'geometry', 'geometry')left_csaMap.head()left_points_in_poly.drop('geometry', axis=1)[['pointsinpolygon']].head(20)<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

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        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>pointsinpolygon</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>18</td>
    </tr>
    <tr>
      <th>1</th>
      <td>0</td>
    </tr>
    <tr>
      <th>2</th>
      <td>67</td>
    </tr>
    <tr>
      <th>3</th>
      <td>0</td>
    </tr>
    <tr>
      <th>4</th>
      <td>1536</td>
    </tr>
    <tr>
      <th>5</th>
      <td>18</td>
    </tr>
    <tr>
      <th>6</th>
      <td>32</td>
    </tr>
    <tr>
      <th>7</th>
      <td>8</td>
    </tr>
    <tr>
      <th>8</th>
      <td>1</td>
    </tr>
    <tr>
      <th>9</th>
      <td>65</td>
    </tr>
    <tr>
      <th>10</th>
      <td>0</td>
    </tr>
    <tr>
      <th>11</th>
      <td>0</td>
    </tr>
    <tr>
      <th>12</th>
      <td>13</td>
    </tr>
    <tr>
      <th>13</th>
      <td>1764</td>
    </tr>
    <tr>
      <th>14</th>
      <td>0</td>
    </tr>
    <tr>
      <th>15</th>
      <td>760</td>
    </tr>
    <tr>
      <th>16</th>
      <td>0</td>
    </tr>
    <tr>
      <th>17</th>
      <td>85</td>
    </tr>
    <tr>
      <th>18</th>
      <td>1253</td>
    </tr>
    <tr>
      <th>19</th>
      <td>2</td>
    </tr>
  </tbody>
</table>
</div>right_points_in_poly.drop('geometry', axis=1)[['pointsinpolygon']].head(20)<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

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        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>pointsinpolygon</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>18</td>
    </tr>
    <tr>
      <th>1</th>
      <td>0</td>
    </tr>
    <tr>
      <th>2</th>
      <td>67</td>
    </tr>
    <tr>
      <th>3</th>
      <td>0</td>
    </tr>
    <tr>
      <th>4</th>
      <td>1536</td>
    </tr>
    <tr>
      <th>5</th>
      <td>18</td>
    </tr>
    <tr>
      <th>6</th>
      <td>32</td>
    </tr>
    <tr>
      <th>7</th>
      <td>8</td>
    </tr>
    <tr>
      <th>8</th>
      <td>1</td>
    </tr>
    <tr>
      <th>9</th>
      <td>65</td>
    </tr>
    <tr>
      <th>10</th>
      <td>0</td>
    </tr>
    <tr>
      <th>11</th>
      <td>0</td>
    </tr>
    <tr>
      <th>12</th>
      <td>13</td>
    </tr>
    <tr>
      <th>13</th>
      <td>1764</td>
    </tr>
    <tr>
      <th>14</th>
      <td>0</td>
    </tr>
    <tr>
      <th>15</th>
      <td>760</td>
    </tr>
    <tr>
      <th>16</th>
      <td>0</td>
    </tr>
    <tr>
      <th>17</th>
      <td>85</td>
    </tr>
    <tr>
      <th>18</th>
      <td>1253</td>
    </tr>
    <tr>
      <th>19</th>
      <td>2</td>
    </tr>
  </tbody>
</table>
</div>left_points_in_poly.plot(column='pointsinpolygon', legend=True)<matplotlib.axes._subplots.AxesSubplot at 0x7fca535fbbe0><Figure size 432x288 with 2 Axes>right_points_in_poly.plot(column='pointsinpolygon', legend=True)<matplotlib.axes._subplots.AxesSubplot at 0x7fca51773978><Figure size 432x288 with 2 Axes>right_points_in_polyscooterdfClean = left_points_in_poly.merge(right_points_in_poly, left_on='CSA2010', right_on='CSA2010', how='left')scooterdfClean.columnsright_points_in_poly.columnsIndex(['OBJECTID', 'CSA2010', 'tpop10', 'male10', 'female10', 'paa17',
       'pwhite17', 'pasi17', 'p2more17', 'ppac17', 'phisp17', 'racdiv17',
       'age5_17', 'age18_17', 'age24_17', 'age64_17', 'age65_17', 'hhs10',
       'femhhs17', 'fam17', 'hhsize10', 'mhhi17', 'hh25inc17', 'hh40inc17',
       'hh60inc17', 'hh75inc17', 'hhm7517', 'hhpov17', 'hhchpov17',
       'Shape__Area', 'Shape__Length', 'geometry', 'pointsinpolygon'],
      dtype='object')right_points_in_poly.head()right_points_in_poly.plot('pointsinpolygon', legend=True)<matplotlib.axes._subplots.AxesSubplot at 0x7fca55083ac8><Figure size 432x288 with 2 Axes>import altair as alt
import geopandas as gpd
import gpdvega
# GeoDataFrame could be passed as usual pd.DataFrame
chart = alt.Chart(right_points_in_poly).mark_geoshape(
).project(
).encode(
    color='pointsinpolygon', # shorthand infer types as for regular pd.DataFrame
    tooltip=['CSA2010','pointsinpolygon'] # GeoDataFrame.index is accessible as id
).properties(
    width=500,
    height=300
) 

routes = alt.Chart(gdf).mark_geoshape(
    filled=False,
    strokeWidth=2
)

chart + routes

chart.save(fileName[:-8]+'.html', embed_options={'renderer':'svg'})
chart.save(fileName[:-8]+'.json')
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0.387096774193548, "trip_count_percent": "0.0%", "trip_count_sum": 12, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.642785, 39.283256], [-76.642075, 39.283294]]}}, {"color": "rgb(205, 222, 236)", "id": 150159710, "streetname": "", "trip_count_average": 1.51612903225806, "trip_count_percent": "0.1%", "trip_count_sum": 47, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.579194, 39.280414], [-76.579178, 39.280235]]}}, {"color": "rgb(194, 214, 232)", "id": 150156733, "streetname": "", "trip_count_average": 2.35483870967742, "trip_count_percent": "0.1%", "trip_count_sum": 73, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.615014, 39.286567], [-76.615006, 39.286544]]}}, {"color": "rgb(217, 231, 241)", "id": 150198087, "streetname": "East Madison Street", "trip_count_average": 0.806451612903226, "trip_count_percent": "0.0%", "trip_count_sum": 25, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.574753, 39.300209], [-76.574555, 39.300216], [-76.573906, 39.300239]]}}, {"color": "rgb(212, 227, 239)", "id": 150153554, "streetname": "Emory Street", "trip_count_average": 1.09677419354839, "trip_count_percent": "0.0%", "trip_count_sum": 34, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.624718, 39.286167], [-76.624703, 39.285686]]}}, {"color": "rgb(192, 212, 231)", "id": 150217367, "streetname": "Perring Parkway", "trip_count_average": 2.58064516129032, "trip_count_percent": "0.1%", "trip_count_sum": 80, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.582177, 39.35028], [-76.581772, 39.350574], [-76.581437, 39.350841]]}}, {"color": "rgb(219, 232, 242)", "id": 150191249, "streetname": "East Chase Street", "trip_count_average": 0.741935483870968, "trip_count_percent": "0.0%", "trip_count_sum": 23, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.607644, 39.302707], [-76.607293, 39.302719]]}}, {"color": "rgb(211, 226, 238)", "id": 150188138, "streetname": "North Frederick Street", "trip_count_average": 1.12903225806452, "trip_count_percent": "0.0%", "trip_count_sum": 35, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.607789, 39.291355], [-76.607728, 39.290618]]}}, {"color": "rgb(194, 213, 231)", "id": 149970366, "streetname": "Reisterstown Road", "trip_count_average": 2.41935483870968, "trip_count_percent": "0.1%", "trip_count_sum": 75, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.700181, 39.354648], [-76.699799, 39.354381]]}}, {"color": "rgb(220, 233, 242)", "id": 150217175, "streetname": "", "trip_count_average": 0.67741935483871, "trip_count_percent": "0.0%", "trip_count_sum": 21, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.582444, 39.344722], [-76.582306, 39.344669], [-76.582131, 39.344589], [-76.581864, 39.34447]]}}, {"color": "rgb(143, 147, 197)", "id": 150156210, "streetname": "", "trip_count_average": 17.2903225806452, "trip_count_percent": "0.7%", "trip_count_sum": 536, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.610001, 39.28083], [-76.609719, 39.280834]]}}, {"color": "rgb(224, 236, 244)", "id": 150184120, "streetname": "North Caroline Street", "trip_count_average": 0.516129032258065, "trip_count_percent": "0.0%", "trip_count_sum": 16, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.59797, 39.304157], [-76.597901, 39.303077]]}}, {"color": "rgb(168, 194, 221)", "id": 150196766, "streetname": "", "trip_count_average": 5.80645161290323, "trip_count_percent": "0.2%", "trip_count_sum": 180, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.618561, 39.327087], [-76.61847, 39.327171], [-76.618432, 39.327201]]}}, {"color": "rgb(169, 194, 221)", "id": 150153354, "streetname": "MD 295 North", "trip_count_average": 5.64516129032258, "trip_count_percent": "0.2%", "trip_count_sum": 175, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.623421, 39.280483], [-76.623353, 39.280635]]}}, {"color": "rgb(152, 172, 210)", "id": 150156452, "streetname": "", "trip_count_average": 10.5806451612903, "trip_count_percent": "0.4%", "trip_count_sum": 328, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.617798, 39.284133], [-76.617806, 39.284065], [-76.617783, 39.283969]]}}, {"color": "rgb(178, 202, 225)", "id": 150162339, "streetname": "Hudson Street", "trip_count_average": 4.2258064516129, "trip_count_percent": "0.2%", "trip_count_sum": 131, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.567833, 39.282516], [-76.567696, 39.28252], [-76.567444, 39.282531]]}}, {"color": "rgb(186, 208, 229)", "id": 150159328, "streetname": "South Washington Street", "trip_count_average": 3.12903225806452, "trip_count_percent": "0.1%", "trip_count_sum": 97, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.58873, 39.284759], [-76.5887, 39.284313]]}}, {"color": "rgb(188, 209, 229)", "id": 150159833, "streetname": "", "trip_count_average": 2.93548387096774, "trip_count_percent": "0.1%", "trip_count_sum": 91, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.581383, 39.281337], [-76.581307, 39.281444]]}}, {"color": "rgb(225, 236, 244)", "id": 150201332, "streetname": "East Preston Street", "trip_count_average": 0.483870967741935, "trip_count_percent": "0.0%", "trip_count_sum": 15, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.582711, 39.305812], [-76.582634, 39.305816], [-76.582215, 39.305831]]}}, {"color": "rgb(186, 208, 229)", "id": 150188519, "streetname": "East Saratoga Street", "trip_count_average": 3.12903225806452, "trip_count_percent": "0.1%", "trip_count_sum": 97, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.615258, 39.292289], [-76.614899, 39.292301]]}}, {"color": "rgb(217, 231, 241)", "id": 149974514, "streetname": "Clipper Park Road", "trip_count_average": 0.806451612903226, "trip_count_percent": "0.0%", "trip_count_sum": 25, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.648209, 39.331081], [-76.647767, 39.331172]]}}, {"color": "rgb(140, 126, 186)", "id": 150195291, "streetname": "Maryland Avenue", "trip_count_average": 24.2903225806452, "trip_count_percent": "1.0%", "trip_count_sum": 753, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.618042, 39.312675], [-76.61802, 39.312164]]}}, {"color": "rgb(232, 242, 247)", "id": 150199324, "streetname": "", "trip_count_average": 0.258064516129032, "trip_count_percent": "0.0%", "trip_count_sum": 8, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.578919, 39.292121], [-76.578911, 39.292041]]}}, {"color": "rgb(207, 223, 237)", "id": 150187820, "streetname": "Dolphin Street", "trip_count_average": 1.35483870967742, "trip_count_percent": "0.1%", "trip_count_sum": 42, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.62262, 39.304103], [-76.622376, 39.304141]]}}, {"color": "rgb(139, 105, 176)", "id": 150155614, "streetname": "", "trip_count_average": 32.4193548387097, "trip_count_percent": "1.3%", "trip_count_sum": 1005, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.610665, 39.280818], [-76.610589, 39.280807], [-76.610245, 39.280803], [-76.6101, 39.280803]]}}, {"color": "rgb(148, 164, 205)", "id": 150188243, "streetname": "", "trip_count_average": 12.6129032258065, "trip_count_percent": "0.5%", "trip_count_sum": 391, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.617661, 39.289268], [-76.617661, 39.289337]]}}, {"color": "rgb(211, 226, 238)", "id": 150159702, "streetname": "South Duncan Street", "trip_count_average": 1.12903225806452, "trip_count_percent": "0.0%", "trip_count_sum": 35, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.586487, 39.286186], [-76.586487, 39.286254]]}}, {"color": "rgb(194, 214, 232)", "id": 150190292, "streetname": "East Madison Street", "trip_count_average": 2.35483870967742, "trip_count_percent": "0.1%", "trip_count_sum": 73, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.612374, 39.298721], [-76.61155, 39.298744]]}}, {"color": "rgb(173, 197, 223)", "id": 150153588, "streetname": "", "trip_count_average": 5.0, "trip_count_percent": "0.2%", "trip_count_sum": 155, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.624459, 39.286258], [-76.624405, 39.286262], [-76.623932, 39.286277], [-76.62362, 39.286289]]}}]}}, {"mode": "vega-lite"});
</script><h2>Inspect Origins-Destinations Data</h2>
ls 'Trip origins-destinations by month'# @title Example form fields
#@markdown Forms support many types of fields.
fileName = 'Trip Destinations by block August 2019.geojson'  #@param ['Trip Destinations by block August 2019.geojson', 'Trip Destinations by block December 2019.geojson', 'Trip Destinations by block November 2019.geojson', 'Trip Destinations by block October 2019.geojson', 'Trip Destinations by block September 2019.geojson', 'Trip Origins by block August 2019.geojson', 'Trip Origins by block December 2019.geojson', 'Trip Origins by block November 2019.geojson', 'Trip Origins by block October 2019.geojson', 'Trip Origins by block September 2019.geojson']
columnName = "name"  #@param ['name', 'value', 'color', 'radius']

#@markdown ---

gdf = gpd.read_file( findFile('./', fileName) )

gdf.plot( column = columnName)
gdf.columns
gdf[['id','name', 'value', 'color', 'radius']].head(5)dxp.bar(x='color', y='value', data=gdf, aggfunc='median')dxp.scatter(x = "color", y = "value", data = gdf, aggfunc = "median")#hide
!pip install nbdev
from nbdev import *
! nbdev_upgrade<h2>Introduction</h2>
<p><a href="https://mybinder.org/v2/gh/karpatic/datalab/master?filepath=%2Fnotebooks%2F02_scooterExploration.ipynb"><img src="https://mybinder.org/badge_logo.svg" alt="Binder" /></a>
<a href="https://colab.research.google.com/github/karpatic/datalab/blob/master/notebooks/02_scooterExploration.ipynb"><img src="https://pete88b.github.io/fastpages/assets/badges/colab.svg" alt="Binder" /></a>
<a href="https://github.com/karpatic/datalab/tree/master/notebooks/02_scooterExploration.ipynb"><img src="https://pete88b.github.io/fastpages/assets/badges/github.svg" alt="Binder" /></a>
<a href="https://github.com/ellerbrock/open-source-badges/"><img src="https://badges.frapsoft.com/os/v3/open-source.svg?v=103" alt="Open Source Love svg3" /></a></p>
<p><a href="https://github.com/karpatic/datalabs/blob/master/LICENSE"><img src="https://img.shields.io/npm/l/all-contributors.svg?style=flat" alt="NPM License" /></a>
<a href="https://karpatic.github.io"><img src="http://img.shields.io/badge/Status-Active-green.svg" alt="Active" /></a>
<a href="https://pypi.python.org/pypi/dataplay/"><img src="https://img.shields.io/pypi/pyversions/dataplay.svg" alt="Python Versions" /></a>
<a href=""><img src="https://img.shields.io/github/last-commit/karpatic/datalabs.svg?style=flat" alt="GitHub last commit" /></a>
<a href="http://unmaintained.tech/"><img src="http://unmaintained.tech/badge.svg" alt="No Maintenance Intended" /></a> </p>
<p><a href="https://github.com/karpatic/datalabs"><img src="https://img.shields.io/github/stars/karpatic/datalabs.svg?style=social&amp;label=Star" alt="GitHub stars" /></a>
<a href="https://github.com/karpatic/datalabs"><img src="https://img.shields.io/github/watchers/karpatic/datalabs.svg?style=social&amp;label=Watch" alt="GitHub watchers" /></a>
<a href="https://github.com/karpatic/datalabs"><img src="https://img.shields.io/github/forks/karpatic/datalabs.svg?style=social&amp;label=Fork" alt="GitHub forks" /></a>
<a href="https://github.com/karpatic/datalabs"><img src="https://img.shields.io/github/followers/karpatic.svg?style=social&amp;label=Follow" alt="GitHub followers" /></a> </p>
<p><a href="https://twitter.com/intent/tweet?text=Check%20out%20this%20%E2%9C%A8%20colab%20by%20@bniajfi%20https://github.com/karpatic/datalabs%20%F0%9F%A4%97"><img src="https://img.shields.io/twitter/url/https/github.com/karpatic/datalabs.svg?style=social" alt="Tweet" /></a>
<a href="https://twitter.com/bniajfi"><img src="https://img.shields.io/twitter/follow/bniajfi.svg?style=social" alt="Twitter Follow" /></a></p>
<p>This chapter has brought to you in collaboration by Brian Kelly, Michael Vandi, Logan Shertz, and Charles Karpati.</p>
<p><strong>Dataset:</strong>
Scooter data: </p>
<ul>
<li>Routes: 3 months (September to August 2019)</li>
<li>Deployment/</li>
<li>Routes</li>
<li>Trip origins-destinations by month</li>
</ul>
# do you see this? - Carlos# Hello world! - Michael# Checking - Brian<h2>Local File Access (Optional)</h2>
# hide_output
# (Optional) Run this cell to gain access to Google Drive (Colabs only) 
from google.colab import drive

# Colabs operates in a virtualized enviornment
# Colabs default directory is at ~/content.
# We mount Drive into a temporary folder at '~/content/drive' 

drive.mount('/content/drive')Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly&response_type=code

Enter your authorization code:
··········
Mounted at /content/drive
<p>File path's will vary</p>
# cd ./drive/'My Drive'/BNIA/responsive_records/Routes# cd ../content/drive/My Drive/DATA/scooter/content/drive/My Drive/DATA/scooter
# Michael's Directory
# cd ./drive/'My Drive'/BNIA/'Scooter Use Data'/BNIAcd ./Routes/content/drive/My Drive/DATA/scooter/Routes
# hide_output
! ls leftRouts.csv			 'Routing November 2019.geojson'
 rightRouts.csv			 'Routing October 2019.geojson'
'Routing August 2019.geojson'	 'Routing September 2019.geojson'
'Routing December 2019.geojson'
# hide_output
!cd ../ && ls boundsdf.csv	 rightRouts.csv  'Trip origins-destinations by month'
 Deployment	 Routes
 leftRouts.csv	 scooterdf.csv
<h1><strong>Installs</strong></h1>
! pip install dexplot
!pip install folium
!pip install geopandas
!pip install ipyleaflet
!pip install gpdvega
!pip install dataplay<h1><strong>Imports</strong></h1>
import os
import pandas as pd
import geopandas as gpd
import dexplot as dxp
import folium as fol
import json
import altair as alt
import gpdvega
import dataplay
# These imports will handle everything
import os
import sys
import csv
from IPython.display import clear_output
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import geopandas as gpd
from geopandas import GeoDataFrame
import psycopg2
import pyproj
from pyproj import Proj, transform
# conda install -c conda-forge proj4
from shapely.geometry import Point
from shapely import wkb
from shapely.wkt import loads
# https://pypi.org/project/geopy/
from geopy.geocoders import Nominatim
import folium
from folium import plugins

from dataplay.merge import mergeDatasets 

import dexplot as dxp

# In case file is KML, enable support
import fiona
fiona.drvsupport.supported_drivers['kml'] = 'rw'
fiona.drvsupport.supported_drivers['KML'] = 'rw'

#this cell is good to copy
from shapely.geometry import LineString
pd.plotting.register_matplotlib_converters()

from dataplay.geoms import readInGeometryData
from shapely import wkt
from dataplay.geoms import readInGeometryData

import ipywidgets as widgets
!jupyter nbextension enable --py widgetsnbextension
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = 'all'
import ipywidgets as widgets
from ipywidgets import interact, interact_manual
alt.data_transformers.enable('default', max_rows=None)<h3>Convenience Functions</h3>
# Return the boundaries of a Linestring 
def split(geom):
  print( str( type(geom)) )
  #Linestring might not be the actual type, so this may need to be fied. 
  #IF its not a linestring then dont run it and stuff 'false' and the datatype
  if str( type(geom)) == "<class 'shapely.geometry.linestring.LineString'>" and not str(geom.boundary) == 'MULTIPOINT EMPTY':
    print(geom.boundary)
    left, right = geom.boundary
    print('left x: ', type(left.x), left.x)
    print('left y: ', type(left.y), left.y)
    print('right x: ', type(right.x), right.x)
    print('right y: ', type(right.y), right.y)
    return left.x, left.y, right.x, right.y
  else: return False, type(geom), False, type(geom)# To 'import' a script you wrote, map its filepath into the sys
def getPointsInPolygons(pts, polygons, ptsCoordCol, polygonsCoordCol): 
    count = 0 
    total = pts.size / len(pts.columns)
    # We're going to keep a list of how many points we find.
    pts_in_polys = []

    # Loop over polygons with index i.
    for i, poly in polygons.iterrows():
        # print('Searching for point within Geom:', poly )
        # Keep a list of points in this poly
        pts_in_this_poly = []

        # Now loop over all points with index j.
        for j, pt in pts.iterrows():
          if poly[polygonsCoordCol].contains(pt[ptsCoordCol]):
            # Then it's a hit! Add it to the list,
            # and drop it so we have less hunting.
            pts_in_this_poly.append(pt[ptsCoordCol])
            pts = pts.drop([j])

        # We could do all sorts, like grab a property of the
        # points, but let's just append the number of them.
        pts_in_polys.append(len(pts_in_this_poly))
        print('Found this many points within the Geom:', len(pts_in_this_poly) ) 
        count = count + len(pts_in_this_poly) 
        clear_output(wait=True)

    # Add the number of points for each poly to the dataframe.
    polygons['pointsinpolygon'] = gpd.GeoSeries(pts_in_polys)
    print( 'Total Points: ', total )
    print( 'Total Points in Polygons: ', count )
    print( 'Prcnt Points in Polygons: ', count / total )
    return polygons<h3>File Access Convenience Functions</h3>
# Find Relative Path to Files
def findFile(root, file):
    for d, subD, f in os.walk(root):
        if file in f:
            return "{1}/{0}".format(file, d)
            break 

# To 'import' a script you wrote, map its filepath into the sys
def addPath(root, file): sys.path.append(os.path.abspath( findFile( './', file) ))findFile('./', 'Routing September 2019.geojson')'.//Routing September 2019.geojson'ls leftRouts.csv                   'Routing November 2019.geojson'
 rightRouts.csv                  'Routing October 2019.geojson'
'Routing August 2019.geojson'    'Routing September 2019.geojson'
'Routing December 2019.geojson'
<h2>Inspect Deployment Data</h2>
ls 'Trip origins-destinations by month''Trip Destinations by block August 2019.geojson'
'Trip Destinations by block December 2019.geojson'
'Trip Destinations by block November 2019.geojson'
'Trip Destinations by block October 2019.geojson'
'Trip Destinations by block September 2019.geojson'
'Trip Origins by block August 2019.geojson'
'Trip Origins by block December 2019.geojson'
'Trip Origins by block November 2019.geojson'
'Trip Origins by block October 2019.geojson'
'Trip Origins by block September 2019.geojson'
#@title Example form fields
#@markdown Forms support many types of fields.
fileName = "Trip Origins by block September 2019.geojson"  #@param ['Daily Deployment average by block August 2019.csv', 'Daily Deployment average by block December 2019.csv', 'Daily Deployment average by block November 2019.csv', 'Daily Deployment average by block October 2019.csv', 'Daily Deployment average by block September 2019.csv', 'Trip Destinations by block August 2019.geojson','Trip Destinations by block December 2019.geojson','Trip Destinations by block November 2019.geojson', 'Trip Destinations by block October 2019.geojson', 'Trip Destinations by block September 2019.geojson', 'Trip Origins by block August 2019.geojson','Trip Origins by block December 2019.geojson','Trip Origins by block November 2019.geojson','Trip Origins by block October 2019.geojson','Trip Origins by block September 2019.geojson']
#@markdown ---gdf = gpd.read_file( findFile('./', fileName) )
gdf.head()<div>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>id</th>
      <th>name</th>
      <th>color</th>
      <th>radius</th>
      <th>value</th>
      <th>geometry</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>1</td>
      <td>Block 245102502063018</td>
      <td>#fff</td>
      <td>4.999286</td>
      <td>NaN</td>
      <td>POLYGON ((-76.66168 39.26342, -76.66136 39.26373, -76.66096 39.26343, -76.66041 39.26306, -76.66053 39.26296, -76.66070 39.26281, -76.66077 39.26284, -76.66100 39.26296, -76.66117 39.26306, -76.66129 39.26315, -76.66141 39.26322, -76.66168 39.26342))</td>
    </tr>
    <tr>
      <th>1</th>
      <td>2</td>
      <td>Block 245102006001022</td>
      <td>#fff</td>
      <td>4.999286</td>
      <td>NaN</td>
      <td>POLYGON ((-76.66319 39.28427, -76.66306 39.28436, -76.66301 39.28439, -76.66293 39.28439, -76.66271 39.28418, -76.66303 39.28402, -76.66317 39.28417, -76.66320 39.28423, -76.66319 39.28427))</td>
    </tr>
    <tr>
      <th>2</th>
      <td>3</td>
      <td>Block 245102804033017</td>
      <td>#fff</td>
      <td>4.999286</td>
      <td>NaN</td>
      <td>POLYGON ((-76.71122 39.27927, -76.71121 39.27975, -76.71121 39.27990, -76.71119 39.28076, -76.71045 39.27932, -76.71045 39.27920, -76.71056 39.27907, -76.71123 39.27880, -76.71122 39.27927))</td>
    </tr>
    <tr>
      <th>3</th>
      <td>4</td>
      <td>Block 245102716006003</td>
      <td>#fff</td>
      <td>4.999286</td>
      <td>NaN</td>
      <td>POLYGON ((-76.66960 39.34512, -76.66905 39.34526, -76.66886 39.34531, -76.66848 39.34539, -76.66835 39.34502, -76.66867 39.34498, -76.66924 39.34484, -76.66946 39.34475, -76.66960 39.34512))</td>
    </tr>
    <tr>
      <th>4</th>
      <td>5</td>
      <td>Block 245102709023013</td>
      <td>#fff</td>
      <td>4.999286</td>
      <td>NaN</td>
      <td>POLYGON ((-76.58976 39.35401, -76.58972 39.35415, -76.58966 39.35435, -76.58964 39.35447, -76.58960 39.35469, -76.58940 39.35467, -76.58925 39.35465, -76.58912 39.35465, -76.58902 39.35464, -76.58898 39.35464, -76.58906 39.35425, -76.58917 39.35385, -76.58925 39.35385, -76.58939 39.35386, -76.58979 39.35393, -76.58976 39.35401))</td>
    </tr>
  </tbody>
</table>
</div>gdf.plot(column = 'value')<matplotlib.axes._subplots.AxesSubplot at 0x7f2c97160da0><Figure size 432x288 with 1 Axes>point_df = gdf.copy()
point_df['centroids'] = df.centroid
point_df = point_df.drop(columns = 'geometry')
point_df = point_df.set_geometry('centroids')
point_df.head(1)
point_df.plot(marker='o', color='red', markersize='value')point_df.value.hist()point_df[point_df.value > 1000].plot()baltimore = gpd.read_file("https://opendata.arcgis.com/datasets/b738a8587b6d479a8824d937892701d8_0.geojson")from dataplay.geoms import workWithGeometryDatabaltimore.columnsIndex(['OBJECTID', 'CSA2010', 'tpop10', 'male10', 'female10', 'paa17',
       'pwhite17', 'pasi17', 'p2more17', 'ppac17', 'phisp17', 'racdiv17',
       'age5_17', 'age18_17', 'age24_17', 'age64_17', 'age65_17', 'hhs10',
       'femhhs17', 'fam17', 'hhsize10', 'mhhi17', 'hh25inc17', 'hh40inc17',
       'hh60inc17', 'hh75inc17', 'hhm7517', 'hhpov17', 'hhchpov17',
       'Shape__Area', 'Shape__Length', 'geometry'],
      dtype='object')pointsInPolys = workWithGeometryData(method = 'ponp', df = point_df, polys = baltimore, ptsCoordCol ='centroids', 
                                     polygonsCoordCol = 'geometry', polygonsLabel = 'CSA2010')
pointsInPolys.plot(column='value', legend=True, markersize = 1)Total Points:  13506.0
Total Points in Polygons:  13453
Prcnt Points in Polygons:  0.9960758181548941
<matplotlib.axes._subplots.AxesSubplot at 0x7ff7c35c92e8><Figure size 432x288 with 2 Axes>ponp_copy = pointsInPolys.copy()ponp_copy.value.isnull().groupby([ponp_copy['CSA2010']]).sum().astype(int).reset_index(name='NumberMissingCount').head()<div>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>CSA2010</th>
      <th>NumberMissingCount</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>Allendale/Irvington/S. Hilton</td>
      <td>290</td>
    </tr>
    <tr>
      <th>1</th>
      <td>Beechfield/Ten Hills/West Hills</td>
      <td>180</td>
    </tr>
    <tr>
      <th>2</th>
      <td>Belair-Edison</td>
      <td>250</td>
    </tr>
    <tr>
      <th>3</th>
      <td>Brooklyn/Curtis Bay/Hawkins Point</td>
      <td>580</td>
    </tr>
    <tr>
      <th>4</th>
      <td>Canton</td>
      <td>0</td>
    </tr>
  </tbody>
</table>
</div>ponp_copy.value.notnull().groupby([ponp_copy['CSA2010']]).sum().astype(int) / ponp_copy.value.isnull().groupby([ponp_copy['CSA2010']]).sum().astype(int)ponp_copy.value.isnull().groupby([ponp_copy['CSA2010']]).sum().astype(int) / ponp_copy.value.groupby([ponp_copy['CSA2010']]).sum().astype(int) * 100ponp_copy.fillna(-1).groupby('CSA2010')['value'].sum()gdf.value.unique()gdf.value.describe()count    5677.000000
mean     40.862603  
std      175.769408 
min      1.000000   
25%      2.000000   
50%      5.000000   
75%      21.000000  
max      6978.000000
Name: value, dtype: float64gdf.value.var() # Return unbiased variance over requested axis.30894.884924369213gdf.value.sem() # Return unbiased standard error of the mean over requested axis.2.332834040372481gdf.value.nunique()	# Count distinct observations over requested axis.364# DataFrame.shape	Return a tuple representing the dimensionality of the DataFrame.
gdf.shape

#DataFrame.size	Return an int representing the number of elements in this object.
gdf.size

# DataFrame.ndim	Return an integer representing the number of axes/array dimensions.
gdf.ndim

# Note Used : 
# DataFrame.axes	Return a list representing the axes of the DataFrame.

gdf.dtypes

# Return unbiased kurtosis over requested axis using Fisher’s definition of kurtosis (kurtosis of normal == 0.0).
gdf.kurtosis()(13506, 6)810362id          object  
name        object  
color       object  
radius      float64 
value       float64 
geometry    geometry
dtype: objectid       -1.200000   
radius    1132.851562
value     495.784213 
dtype: float64<h3>Load Tracts</h3>
# This will just beautify the output.

pd.set_option('display.expand_frame_repr', False)
pd.set_option('display.precision', 2)
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"

pd.set_option('max_colwidth', 20)# The attributes are what we will use.
in_crs = 2248 # The CRS we recieve our data.
out_crs = 4326 # The CRS we would like to have our data represented as.
geom = 'geometry' # The column where our spatial information lives.

# A Url to load
boundariesBaltimoreTractsNoWater2010 = "https://docs.google.com/spreadsheets/d/e/2PACX-1vQ8xXdUaT17jkdK0MWTJpg3GOy6jMWeaXTlguXNjCSb8Vr_FanSZQRaTU-m811fQz4kyMFK5wcahMNY/pub?gid=886223646&single=true&output=csv"

# Read in the dataframe
gdf = pd.read_csv(boundariesBaltimoreTractsNoWater2010)

# Convert the geometry column datatype from a string of text into a coordinate datatype.
gdf['geometry'] = gdf['geometry'].apply(lambda x: loads( str(x) ))

# Process the dataframe as a geodataframe with a known CRS and geom column.
gdf = GeoDataFrame(gdf, crs=in_crs, geometry='geometry')boundariesBaltimoreTractsNoWater2010.head()<p>Ensure merge is on consistent datatypes</p>
gdf['GEOID10'] = gdf['GEOID10'].astype(str)
scooterdf['nameChange2'] = scooterdf['nameChange2'].astype(str)<p>Perform the merge</p>
scooterdfClean = gdf.merge(scooterdf, left_on='GEOID10', right_on='nameChange2').drop(['name', 'nameChange1', 'nameChange2'], axis=1)scooterdfClean.head()# scooterdf.to_csv('./scooterdf.csv', index=False)
# gdf.drop(columns='geometry').to_csv('./boundsdf.csv', index=False)lsscooterdfClean.groupby('CSA')['value'].sum()scooterdfClean.value.isna().groupby([scooterdfClean['CSA']]).sum().astype(int).reset_index(name='notApplicable')scooterdfClean.value.notnull().groupby([scooterdfClean['CSA']]).sum().astype(int).reset_index(name='NotMissingCount')scooterdfClean.value.isnull().groupby([scooterdfClean['CSA']]).sum().astype(int).reset_index(name='NumberMissingCount')scooterdfClean.fillna(-1).groupby('CSA')['value'].sum()scooterdfClean.groupby('CSA')['value'].mean()scooterdfClean.groupby('CSA')['value'].sum()scooterdfClean.CSA.value_counts()https://pandas.pydata.org/docs/getting_started/intro_tutorials/06_calculate_statistics.html<h2>Inspect Routes Data</h2>
fileName = 'Routing December 2019.geojson'  #@param ['Routing August 2019.geojson', 'Routing October 2019.geojson', 'Routing December 2019.geojson', 'Routing September 2019.geojson', 'Routing November 2019.geojson']
columnName = "streetname"  #@param ['id', 'color', 'streetname', 'trip_count_sum', 'trip_count_average', 'trip_count_percent']
gdf = gpd.read_file( findFile('./', fileName) )
gdf.head()<div>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>id</th>
      <th>color</th>
      <th>streetname</th>
      <th>trip_count_sum</th>
      <th>trip_count_average</th>
      <th>trip_count_percent</th>
      <th>geometry</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>150197772</td>
      <td>rgb(218, 231, 241)</td>
      <td>Jefferson Street</td>
      <td>24</td>
      <td>0.774194</td>
      <td>0.0%</td>
      <td>LINESTRING (-76.58576 39.29660, -76.58550 39.29661)</td>
    </tr>
    <tr>
      <th>1</th>
      <td>150155955</td>
      <td>rgb(164, 190, 219)</td>
      <td></td>
      <td>206</td>
      <td>6.645161</td>
      <td>0.3%</td>
      <td>LINESTRING (-76.61320 39.28136, -76.61318 39.28115)</td>
    </tr>
    <tr>
      <th>2</th>
      <td>150191673</td>
      <td>rgb(204, 221, 236)</td>
      <td>Harford Avenue</td>
      <td>49</td>
      <td>1.580645</td>
      <td>0.1%</td>
      <td>LINESTRING (-76.60169 39.30535, -76.60163 39.30544, -76.60156 39.30555)</td>
    </tr>
    <tr>
      <th>3</th>
      <td>150184657</td>
      <td>rgb(229, 239, 246)</td>
      <td></td>
      <td>11</td>
      <td>0.354839</td>
      <td>0.0%</td>
      <td>LINESTRING (-76.61493 39.29294, -76.61491 39.29258, -76.61490 39.29230)</td>
    </tr>
    <tr>
      <th>4</th>
      <td>150188407</td>
      <td>rgb(169, 194, 221)</td>
      <td></td>
      <td>178</td>
      <td>5.741935</td>
      <td>0.2%</td>
      <td>LINESTRING (-76.61662 39.29053, -76.61661 39.29046)</td>
    </tr>
  </tbody>
</table>
</div>baltimore = gpd.read_file("https://opendata.arcgis.com/datasets/b738a8587b6d479a8824d937892701d8_0.geojson")tst = """background = alt.Chart(gdf).mark_geoshape(
    stroke='white',
    strokeWidth=2
).encode(
    color=alt.value('#eee'),
).properties(
    width=700,
    height=500
)"""

# GeoDataFrame could be passed as usual pd.DataFrame
city = alt.Chart(baltimore).mark_geoshape(
).project(
).encode(
    color='tpop10', # shorthand infer types as for regular pd.DataFrame
    tooltip='CSA2010' # GeoDataFrame.index is accessible as id
).properties(
    width=500,
    height=300
)

routes = alt.Chart(gdf).mark_geoshape(
    filled=False,
    strokeWidth=2
)

city + routes<p>Clean the gdf of empties.</p>
gdf = gdf[~gdf.isna()]
gdf = gdf[~gdf.is_empty]<p>Now get the extremities; this will take a minute.</p>
# hide_output
gdf['lefty'], gdf['leftx'],gdf['righty'], gdf['rightx'] = zip(*gdf["geometry"].map(split))<p>Split the gdf into a left and right dataset</p>
gdf_left = gdf.copy()
gdf_left = gdf_left.drop(columns = ['geometry','rightx', 'righty'])

gdf_right= gdf.copy()
gdf_right = gdf_right.drop(columns = ['geometry','leftx', 'lefty', 'streetname', 'trip_count_sum', 'trip_count_average', 'trip_count_percent', 'color' ])<p>The coordinate variables of object type will cause problems.</p>
gdf_right.dtypesid        int64  
righty    float64
rightx    object 
dtype: object<p>Let's go ahead and coerce a correction.</p>
gdf_right['righty']=pd.to_numeric(gdf_right['righty'], errors='coerce')
gdf_left['lefty']=pd.to_numeric(gdf_left['lefty'], errors='coerce')

gdf_right['rightx']=pd.to_numeric(gdf_right['rightx'], errors='coerce')
gdf_left['leftx']=pd.to_numeric(gdf_left['leftx'], errors='coerce')<p>Now we can view the results</p>
gdf_right.dtypesid        int64  
righty    float64
rightx    float64
dtype: object<p>Save these csv's because it took a minute to get to where we are now.</p>
gdf_right.to_csv('rightRouts.csv')
gdf_left.to_csv('leftRouts.csv')<p>Convert the datasets to geodataframes for further exploration!</p>
# We could create a geodataframe like this
#temp_gdf = gpd.GeoDataFrame( gdf_right, geometry=gpd.points_from_xy(gdf_right.rightx, gdf_right.righty) )
#temp_gdf.head()

# Alternately this could work.. unfinished.. but wkt.loads can make a Point from text
# gdf_right['strCol']=gdf_right['rightx'].astype(str)
# gdf_right['geometry'] = gdf_right['strCol'].apply(wkt.loads)# hide_output
left_csaMap = readInGeometryData(url=gdf_left, porg='p', geom= False, lat= 'leftx', lng= 'lefty', revgeocode=False, save=False, in_crs=4268, out_crs=4268)# hide_output
right_csaMap = readInGeometryData(url=gdf_right, porg='p', geom= False, lat= 'rightx', lng= 'righty', revgeocode=False, save=False, in_crs=4268, out_crs=4268)right_csaMap.head()<div>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>id</th>
      <th>righty</th>
      <th>rightx</th>
      <th>geometry</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>150197772</td>
      <td>-76.585503</td>
      <td>39.296607</td>
      <td>POINT (-76.58550 39.29661)</td>
    </tr>
    <tr>
      <th>1</th>
      <td>150155955</td>
      <td>-76.613183</td>
      <td>39.281147</td>
      <td>POINT (-76.61318 39.28115)</td>
    </tr>
    <tr>
      <th>2</th>
      <td>150191673</td>
      <td>-76.601555</td>
      <td>39.305545</td>
      <td>POINT (-76.60156 39.30555)</td>
    </tr>
    <tr>
      <th>3</th>
      <td>150184657</td>
      <td>-76.614899</td>
      <td>39.292301</td>
      <td>POINT (-76.61490 39.29230)</td>
    </tr>
    <tr>
      <th>4</th>
      <td>150188407</td>
      <td>-76.616608</td>
      <td>39.290458</td>
      <td>POINT (-76.61661 39.29046)</td>
    </tr>
  </tbody>
</table>
</div>left_csaMap.head()left_csaMap.plot(column='trip_count_sum')right_csaMap.plot()baltimore.columns# hide_output
right_points_in_poly = getPointsInPolygons(right_csaMap, baltimore, 'geometry', 'geometry')right_csaMap.head()left_points_in_poly = getPointsInPolygons(left_csaMap, baltimore, 'geometry', 'geometry')left_csaMap.head()left_points_in_poly.drop('geometry', axis=1)[['pointsinpolygon']].head(20)<div>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>pointsinpolygon</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>18</td>
    </tr>
    <tr>
      <th>1</th>
      <td>0</td>
    </tr>
    <tr>
      <th>2</th>
      <td>67</td>
    </tr>
    <tr>
      <th>3</th>
      <td>0</td>
    </tr>
    <tr>
      <th>4</th>
      <td>1536</td>
    </tr>
    <tr>
      <th>5</th>
      <td>18</td>
    </tr>
    <tr>
      <th>6</th>
      <td>32</td>
    </tr>
    <tr>
      <th>7</th>
      <td>8</td>
    </tr>
    <tr>
      <th>8</th>
      <td>1</td>
    </tr>
    <tr>
      <th>9</th>
      <td>65</td>
    </tr>
    <tr>
      <th>10</th>
      <td>0</td>
    </tr>
    <tr>
      <th>11</th>
      <td>0</td>
    </tr>
    <tr>
      <th>12</th>
      <td>13</td>
    </tr>
    <tr>
      <th>13</th>
      <td>1764</td>
    </tr>
    <tr>
      <th>14</th>
      <td>0</td>
    </tr>
    <tr>
      <th>15</th>
      <td>760</td>
    </tr>
    <tr>
      <th>16</th>
      <td>0</td>
    </tr>
    <tr>
      <th>17</th>
      <td>85</td>
    </tr>
    <tr>
      <th>18</th>
      <td>1253</td>
    </tr>
    <tr>
      <th>19</th>
      <td>2</td>
    </tr>
  </tbody>
</table>
</div>right_points_in_poly.drop('geometry', axis=1)[['pointsinpolygon']].head(20)<div>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>pointsinpolygon</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>18</td>
    </tr>
    <tr>
      <th>1</th>
      <td>0</td>
    </tr>
    <tr>
      <th>2</th>
      <td>67</td>
    </tr>
    <tr>
      <th>3</th>
      <td>0</td>
    </tr>
    <tr>
      <th>4</th>
      <td>1536</td>
    </tr>
    <tr>
      <th>5</th>
      <td>18</td>
    </tr>
    <tr>
      <th>6</th>
      <td>32</td>
    </tr>
    <tr>
      <th>7</th>
      <td>8</td>
    </tr>
    <tr>
      <th>8</th>
      <td>1</td>
    </tr>
    <tr>
      <th>9</th>
      <td>65</td>
    </tr>
    <tr>
      <th>10</th>
      <td>0</td>
    </tr>
    <tr>
      <th>11</th>
      <td>0</td>
    </tr>
    <tr>
      <th>12</th>
      <td>13</td>
    </tr>
    <tr>
      <th>13</th>
      <td>1764</td>
    </tr>
    <tr>
      <th>14</th>
      <td>0</td>
    </tr>
    <tr>
      <th>15</th>
      <td>760</td>
    </tr>
    <tr>
      <th>16</th>
      <td>0</td>
    </tr>
    <tr>
      <th>17</th>
      <td>85</td>
    </tr>
    <tr>
      <th>18</th>
      <td>1253</td>
    </tr>
    <tr>
      <th>19</th>
      <td>2</td>
    </tr>
  </tbody>
</table>
</div>left_points_in_poly.plot(column='pointsinpolygon', legend=True)<matplotlib.axes._subplots.AxesSubplot at 0x7fca535fbbe0><Figure size 432x288 with 2 Axes>right_points_in_poly.plot(column='pointsinpolygon', legend=True)<matplotlib.axes._subplots.AxesSubplot at 0x7fca51773978><Figure size 432x288 with 2 Axes>right_points_in_polyscooterdfClean = left_points_in_poly.merge(right_points_in_poly, left_on='CSA2010', right_on='CSA2010', how='left')scooterdfClean.columnsright_points_in_poly.columnsIndex(['OBJECTID', 'CSA2010', 'tpop10', 'male10', 'female10', 'paa17',
       'pwhite17', 'pasi17', 'p2more17', 'ppac17', 'phisp17', 'racdiv17',
       'age5_17', 'age18_17', 'age24_17', 'age64_17', 'age65_17', 'hhs10',
       'femhhs17', 'fam17', 'hhsize10', 'mhhi17', 'hh25inc17', 'hh40inc17',
       'hh60inc17', 'hh75inc17', 'hhm7517', 'hhpov17', 'hhchpov17',
       'Shape__Area', 'Shape__Length', 'geometry', 'pointsinpolygon'],
      dtype='object')right_points_in_poly.head()right_points_in_poly.plot('pointsinpolygon', legend=True)<matplotlib.axes._subplots.AxesSubplot at 0x7fca55083ac8><Figure size 432x288 with 2 Axes>import altair as alt
import geopandas as gpd
import gpdvega
# GeoDataFrame could be passed as usual pd.DataFrame
chart = alt.Chart(right_points_in_poly).mark_geoshape(
).project(
).encode(
    color='pointsinpolygon', # shorthand infer types as for regular pd.DataFrame
    tooltip=['CSA2010','pointsinpolygon'] # GeoDataFrame.index is accessible as id
).properties(
    width=500,
    height=300
) 

routes = alt.Chart(gdf).mark_geoshape(
    filled=False,
    strokeWidth=2
)

chart + routes

chart.save(fileName[:-8]+'.html', embed_options={'renderer':'svg'})
chart.save(fileName[:-8]+'.json')
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{"type": "LineString", "coordinates": [[-76.586487, 39.286186], [-76.586487, 39.286254]]}}, {"color": "rgb(194, 214, 232)", "id": 150190292, "streetname": "East Madison Street", "trip_count_average": 2.35483870967742, "trip_count_percent": "0.1%", "trip_count_sum": 73, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.612374, 39.298721], [-76.61155, 39.298744]]}}, {"color": "rgb(173, 197, 223)", "id": 150153588, "streetname": "", "trip_count_average": 5.0, "trip_count_percent": "0.2%", "trip_count_sum": 155, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.624459, 39.286258], [-76.624405, 39.286262], [-76.623932, 39.286277], [-76.62362, 39.286289]]}}]}}, {"mode": "vega-lite"});
</script><h2>Inspect Origins-Destinations Data</h2>
ls 'Trip origins-destinations by month'# @title Example form fields
#@markdown Forms support many types of fields.
fileName = 'Trip Destinations by block August 2019.geojson'  #@param ['Trip Destinations by block August 2019.geojson', 'Trip Destinations by block December 2019.geojson', 'Trip Destinations by block November 2019.geojson', 'Trip Destinations by block October 2019.geojson', 'Trip Destinations by block September 2019.geojson', 'Trip Origins by block August 2019.geojson', 'Trip Origins by block December 2019.geojson', 'Trip Origins by block November 2019.geojson', 'Trip Origins by block October 2019.geojson', 'Trip Origins by block September 2019.geojson']
columnName = "name"  #@param ['name', 'value', 'color', 'radius']

#@markdown ---

gdf = gpd.read_file( findFile('./', fileName) )

gdf.plot( column = columnName)
gdf.columns
gdf[['id','name', 'value', 'color', 'radius']].head(5)dxp.bar(x='color', y='value', data=gdf, aggfunc='median')dxp.scatter(x = "color", y = "value", data = gdf, aggfunc = "median")#hide
!pip install nbdev
from nbdev import *
! nbdev_upgrade<h2>Introduction</h2>
<p><a href="https://mybinder.org/v2/gh/karpatic/datalab/master?filepath=%2Fnotebooks%2F02_scooterExploration.ipynb"><img src="https://mybinder.org/badge_logo.svg" alt="Binder" /></a>
<a href="https://colab.research.google.com/github/karpatic/datalab/blob/master/notebooks/02_scooterExploration.ipynb"><img src="https://pete88b.github.io/fastpages/assets/badges/colab.svg" alt="Binder" /></a>
<a href="https://github.com/karpatic/datalab/tree/master/notebooks/02_scooterExploration.ipynb"><img src="https://pete88b.github.io/fastpages/assets/badges/github.svg" alt="Binder" /></a>
<a href="https://github.com/ellerbrock/open-source-badges/"><img src="https://badges.frapsoft.com/os/v3/open-source.svg?v=103" alt="Open Source Love svg3" /></a></p>
<p><a href="https://github.com/karpatic/datalabs/blob/master/LICENSE"><img src="https://img.shields.io/npm/l/all-contributors.svg?style=flat" alt="NPM License" /></a>
<a href="https://karpatic.github.io"><img src="http://img.shields.io/badge/Status-Active-green.svg" alt="Active" /></a>
<a href="https://pypi.python.org/pypi/dataplay/"><img src="https://img.shields.io/pypi/pyversions/dataplay.svg" alt="Python Versions" /></a>
<a href=""><img src="https://img.shields.io/github/last-commit/karpatic/datalabs.svg?style=flat" alt="GitHub last commit" /></a>
<a href="http://unmaintained.tech/"><img src="http://unmaintained.tech/badge.svg" alt="No Maintenance Intended" /></a> </p>
<p><a href="https://github.com/karpatic/datalabs"><img src="https://img.shields.io/github/stars/karpatic/datalabs.svg?style=social&amp;label=Star" alt="GitHub stars" /></a>
<a href="https://github.com/karpatic/datalabs"><img src="https://img.shields.io/github/watchers/karpatic/datalabs.svg?style=social&amp;label=Watch" alt="GitHub watchers" /></a>
<a href="https://github.com/karpatic/datalabs"><img src="https://img.shields.io/github/forks/karpatic/datalabs.svg?style=social&amp;label=Fork" alt="GitHub forks" /></a>
<a href="https://github.com/karpatic/datalabs"><img src="https://img.shields.io/github/followers/karpatic.svg?style=social&amp;label=Follow" alt="GitHub followers" /></a> </p>
<p><a href="https://twitter.com/intent/tweet?text=Check%20out%20this%20%E2%9C%A8%20colab%20by%20@bniajfi%20https://github.com/karpatic/datalabs%20%F0%9F%A4%97"><img src="https://img.shields.io/twitter/url/https/github.com/karpatic/datalabs.svg?style=social" alt="Tweet" /></a>
<a href="https://twitter.com/bniajfi"><img src="https://img.shields.io/twitter/follow/bniajfi.svg?style=social" alt="Twitter Follow" /></a></p>
<p>This chapter has brought to you in collaboration by Brian Kelly, Michael Vandi, Logan Shertz, and Charles Karpati.</p>
<p><strong>Dataset:</strong>
Scooter data: </p>
<ul>
<li>Routes: 3 months (September to August 2019)</li>
<li>Deployment/</li>
<li>Routes</li>
<li>Trip origins-destinations by month</li>
</ul>
# do you see this? - Carlos# Hello world! - Michael# Checking - Brian<h2>Local File Access (Optional)</h2>
# hide_output
# (Optional) Run this cell to gain access to Google Drive (Colabs only) 
from google.colab import drive

# Colabs operates in a virtualized enviornment
# Colabs default directory is at ~/content.
# We mount Drive into a temporary folder at '~/content/drive' 

drive.mount('/content/drive')Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly&response_type=code

Enter your authorization code:
··········
Mounted at /content/drive
<p>File path's will vary</p>
# cd ./drive/'My Drive'/BNIA/responsive_records/Routes# cd ../content/drive/My Drive/DATA/scooter/content/drive/My Drive/DATA/scooter
# Michael's Directory
# cd ./drive/'My Drive'/BNIA/'Scooter Use Data'/BNIAcd ./Routes/content/drive/My Drive/DATA/scooter/Routes
# hide_output
! ls leftRouts.csv			 'Routing November 2019.geojson'
 rightRouts.csv			 'Routing October 2019.geojson'
'Routing August 2019.geojson'	 'Routing September 2019.geojson'
'Routing December 2019.geojson'
# hide_output
!cd ../ && ls boundsdf.csv	 rightRouts.csv  'Trip origins-destinations by month'
 Deployment	 Routes
 leftRouts.csv	 scooterdf.csv
<h1><strong>Installs</strong></h1>
! pip install dexplot
!pip install folium
!pip install geopandas
!pip install ipyleaflet
!pip install gpdvega
!pip install dataplay<h1><strong>Imports</strong></h1>
import os
import pandas as pd
import geopandas as gpd
import dexplot as dxp
import folium as fol
import json
import altair as alt
import gpdvega
import dataplay
# These imports will handle everything
import os
import sys
import csv
from IPython.display import clear_output
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import geopandas as gpd
from geopandas import GeoDataFrame
import psycopg2
import pyproj
from pyproj import Proj, transform
# conda install -c conda-forge proj4
from shapely.geometry import Point
from shapely import wkb
from shapely.wkt import loads
# https://pypi.org/project/geopy/
from geopy.geocoders import Nominatim
import folium
from folium import plugins

from dataplay.merge import mergeDatasets 

import dexplot as dxp

# In case file is KML, enable support
import fiona
fiona.drvsupport.supported_drivers['kml'] = 'rw'
fiona.drvsupport.supported_drivers['KML'] = 'rw'

#this cell is good to copy
from shapely.geometry import LineString
pd.plotting.register_matplotlib_converters()

from dataplay.geoms import readInGeometryData
from shapely import wkt
from dataplay.geoms import readInGeometryData

import ipywidgets as widgets
!jupyter nbextension enable --py widgetsnbextension
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = 'all'
import ipywidgets as widgets
from ipywidgets import interact, interact_manual
alt.data_transformers.enable('default', max_rows=None)<h3>Convenience Functions</h3>
# Return the boundaries of a Linestring 
def split(geom):
  print( str( type(geom)) )
  #Linestring might not be the actual type, so this may need to be fied. 
  #IF its not a linestring then dont run it and stuff 'false' and the datatype
  if str( type(geom)) == "<class 'shapely.geometry.linestring.LineString'>" and not str(geom.boundary) == 'MULTIPOINT EMPTY':
    print(geom.boundary)
    left, right = geom.boundary
    print('left x: ', type(left.x), left.x)
    print('left y: ', type(left.y), left.y)
    print('right x: ', type(right.x), right.x)
    print('right y: ', type(right.y), right.y)
    return left.x, left.y, right.x, right.y
  else: return False, type(geom), False, type(geom)# To 'import' a script you wrote, map its filepath into the sys
def getPointsInPolygons(pts, polygons, ptsCoordCol, polygonsCoordCol): 
    count = 0 
    total = pts.size / len(pts.columns)
    # We're going to keep a list of how many points we find.
    pts_in_polys = []

    # Loop over polygons with index i.
    for i, poly in polygons.iterrows():
        # print('Searching for point within Geom:', poly )
        # Keep a list of points in this poly
        pts_in_this_poly = []

        # Now loop over all points with index j.
        for j, pt in pts.iterrows():
          if poly[polygonsCoordCol].contains(pt[ptsCoordCol]):
            # Then it's a hit! Add it to the list,
            # and drop it so we have less hunting.
            pts_in_this_poly.append(pt[ptsCoordCol])
            pts = pts.drop([j])

        # We could do all sorts, like grab a property of the
        # points, but let's just append the number of them.
        pts_in_polys.append(len(pts_in_this_poly))
        print('Found this many points within the Geom:', len(pts_in_this_poly) ) 
        count = count + len(pts_in_this_poly) 
        clear_output(wait=True)

    # Add the number of points for each poly to the dataframe.
    polygons['pointsinpolygon'] = gpd.GeoSeries(pts_in_polys)
    print( 'Total Points: ', total )
    print( 'Total Points in Polygons: ', count )
    print( 'Prcnt Points in Polygons: ', count / total )
    return polygons<h3>File Access Convenience Functions</h3>
# Find Relative Path to Files
def findFile(root, file):
    for d, subD, f in os.walk(root):
        if file in f:
            return "{1}/{0}".format(file, d)
            break 

# To 'import' a script you wrote, map its filepath into the sys
def addPath(root, file): sys.path.append(os.path.abspath( findFile( './', file) ))findFile('./', 'Routing September 2019.geojson')'.//Routing September 2019.geojson'ls leftRouts.csv                   'Routing November 2019.geojson'
 rightRouts.csv                  'Routing October 2019.geojson'
'Routing August 2019.geojson'    'Routing September 2019.geojson'
'Routing December 2019.geojson'
<h2>Inspect Deployment Data</h2>
ls 'Trip origins-destinations by month''Trip Destinations by block August 2019.geojson'
'Trip Destinations by block December 2019.geojson'
'Trip Destinations by block November 2019.geojson'
'Trip Destinations by block October 2019.geojson'
'Trip Destinations by block September 2019.geojson'
'Trip Origins by block August 2019.geojson'
'Trip Origins by block December 2019.geojson'
'Trip Origins by block November 2019.geojson'
'Trip Origins by block October 2019.geojson'
'Trip Origins by block September 2019.geojson'
#@title Example form fields
#@markdown Forms support many types of fields.
fileName = "Trip Origins by block September 2019.geojson"  #@param ['Daily Deployment average by block August 2019.csv', 'Daily Deployment average by block December 2019.csv', 'Daily Deployment average by block November 2019.csv', 'Daily Deployment average by block October 2019.csv', 'Daily Deployment average by block September 2019.csv', 'Trip Destinations by block August 2019.geojson','Trip Destinations by block December 2019.geojson','Trip Destinations by block November 2019.geojson', 'Trip Destinations by block October 2019.geojson', 'Trip Destinations by block September 2019.geojson', 'Trip Origins by block August 2019.geojson','Trip Origins by block December 2019.geojson','Trip Origins by block November 2019.geojson','Trip Origins by block October 2019.geojson','Trip Origins by block September 2019.geojson']
#@markdown ---gdf = gpd.read_file( findFile('./', fileName) )
gdf.head()<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>id</th>
      <th>name</th>
      <th>color</th>
      <th>radius</th>
      <th>value</th>
      <th>geometry</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>1</td>
      <td>Block 245102502063018</td>
      <td>#fff</td>
      <td>4.999286</td>
      <td>NaN</td>
      <td>POLYGON ((-76.66168 39.26342, -76.66136 39.26373, -76.66096 39.26343, -76.66041 39.26306, -76.66053 39.26296, -76.66070 39.26281, -76.66077 39.26284, -76.66100 39.26296, -76.66117 39.26306, -76.66129 39.26315, -76.66141 39.26322, -76.66168 39.26342))</td>
    </tr>
    <tr>
      <th>1</th>
      <td>2</td>
      <td>Block 245102006001022</td>
      <td>#fff</td>
      <td>4.999286</td>
      <td>NaN</td>
      <td>POLYGON ((-76.66319 39.28427, -76.66306 39.28436, -76.66301 39.28439, -76.66293 39.28439, -76.66271 39.28418, -76.66303 39.28402, -76.66317 39.28417, -76.66320 39.28423, -76.66319 39.28427))</td>
    </tr>
    <tr>
      <th>2</th>
      <td>3</td>
      <td>Block 245102804033017</td>
      <td>#fff</td>
      <td>4.999286</td>
      <td>NaN</td>
      <td>POLYGON ((-76.71122 39.27927, -76.71121 39.27975, -76.71121 39.27990, -76.71119 39.28076, -76.71045 39.27932, -76.71045 39.27920, -76.71056 39.27907, -76.71123 39.27880, -76.71122 39.27927))</td>
    </tr>
    <tr>
      <th>3</th>
      <td>4</td>
      <td>Block 245102716006003</td>
      <td>#fff</td>
      <td>4.999286</td>
      <td>NaN</td>
      <td>POLYGON ((-76.66960 39.34512, -76.66905 39.34526, -76.66886 39.34531, -76.66848 39.34539, -76.66835 39.34502, -76.66867 39.34498, -76.66924 39.34484, -76.66946 39.34475, -76.66960 39.34512))</td>
    </tr>
    <tr>
      <th>4</th>
      <td>5</td>
      <td>Block 245102709023013</td>
      <td>#fff</td>
      <td>4.999286</td>
      <td>NaN</td>
      <td>POLYGON ((-76.58976 39.35401, -76.58972 39.35415, -76.58966 39.35435, -76.58964 39.35447, -76.58960 39.35469, -76.58940 39.35467, -76.58925 39.35465, -76.58912 39.35465, -76.58902 39.35464, -76.58898 39.35464, -76.58906 39.35425, -76.58917 39.35385, -76.58925 39.35385, -76.58939 39.35386, -76.58979 39.35393, -76.58976 39.35401))</td>
    </tr>
  </tbody>
</table>
</div>gdf.plot(column = 'value')<matplotlib.axes._subplots.AxesSubplot at 0x7f2c97160da0><Figure size 432x288 with 1 Axes>point_df = gdf.copy()
point_df['centroids'] = df.centroid
point_df = point_df.drop(columns = 'geometry')
point_df = point_df.set_geometry('centroids')
point_df.head(1)
point_df.plot(marker='o', color='red', markersize='value')point_df.value.hist()point_df[point_df.value > 1000].plot()baltimore = gpd.read_file("https://opendata.arcgis.com/datasets/b738a8587b6d479a8824d937892701d8_0.geojson")from dataplay.geoms import workWithGeometryDatabaltimore.columnsIndex(['OBJECTID', 'CSA2010', 'tpop10', 'male10', 'female10', 'paa17',
       'pwhite17', 'pasi17', 'p2more17', 'ppac17', 'phisp17', 'racdiv17',
       'age5_17', 'age18_17', 'age24_17', 'age64_17', 'age65_17', 'hhs10',
       'femhhs17', 'fam17', 'hhsize10', 'mhhi17', 'hh25inc17', 'hh40inc17',
       'hh60inc17', 'hh75inc17', 'hhm7517', 'hhpov17', 'hhchpov17',
       'Shape__Area', 'Shape__Length', 'geometry'],
      dtype='object')pointsInPolys = workWithGeometryData(method = 'ponp', df = point_df, polys = baltimore, ptsCoordCol ='centroids', 
                                     polygonsCoordCol = 'geometry', polygonsLabel = 'CSA2010')
pointsInPolys.plot(column='value', legend=True, markersize = 1)Total Points:  13506.0
Total Points in Polygons:  13453
Prcnt Points in Polygons:  0.9960758181548941
<matplotlib.axes._subplots.AxesSubplot at 0x7ff7c35c92e8><Figure size 432x288 with 2 Axes>ponp_copy = pointsInPolys.copy()ponp_copy.value.isnull().groupby([ponp_copy['CSA2010']]).sum().astype(int).reset_index(name='NumberMissingCount').head()<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>CSA2010</th>
      <th>NumberMissingCount</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>Allendale/Irvington/S. Hilton</td>
      <td>290</td>
    </tr>
    <tr>
      <th>1</th>
      <td>Beechfield/Ten Hills/West Hills</td>
      <td>180</td>
    </tr>
    <tr>
      <th>2</th>
      <td>Belair-Edison</td>
      <td>250</td>
    </tr>
    <tr>
      <th>3</th>
      <td>Brooklyn/Curtis Bay/Hawkins Point</td>
      <td>580</td>
    </tr>
    <tr>
      <th>4</th>
      <td>Canton</td>
      <td>0</td>
    </tr>
  </tbody>
</table>
</div>ponp_copy.value.notnull().groupby([ponp_copy['CSA2010']]).sum().astype(int) / ponp_copy.value.isnull().groupby([ponp_copy['CSA2010']]).sum().astype(int)ponp_copy.value.isnull().groupby([ponp_copy['CSA2010']]).sum().astype(int) / ponp_copy.value.groupby([ponp_copy['CSA2010']]).sum().astype(int) * 100ponp_copy.fillna(-1).groupby('CSA2010')['value'].sum()gdf.value.unique()gdf.value.describe()count    5677.000000
mean     40.862603  
std      175.769408 
min      1.000000   
25%      2.000000   
50%      5.000000   
75%      21.000000  
max      6978.000000
Name: value, dtype: float64gdf.value.var() # Return unbiased variance over requested axis.30894.884924369213gdf.value.sem() # Return unbiased standard error of the mean over requested axis.2.332834040372481gdf.value.nunique()	# Count distinct observations over requested axis.364# DataFrame.shape	Return a tuple representing the dimensionality of the DataFrame.
gdf.shape

#DataFrame.size	Return an int representing the number of elements in this object.
gdf.size

# DataFrame.ndim	Return an integer representing the number of axes/array dimensions.
gdf.ndim

# Note Used : 
# DataFrame.axes	Return a list representing the axes of the DataFrame.

gdf.dtypes

# Return unbiased kurtosis over requested axis using Fisher’s definition of kurtosis (kurtosis of normal == 0.0).
gdf.kurtosis()(13506, 6)810362id          object  
name        object  
color       object  
radius      float64 
value       float64 
geometry    geometry
dtype: objectid       -1.200000   
radius    1132.851562
value     495.784213 
dtype: float64<h3>Load Tracts</h3>
# This will just beautify the output.

pd.set_option('display.expand_frame_repr', False)
pd.set_option('display.precision', 2)
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"

pd.set_option('max_colwidth', 20)# The attributes are what we will use.
in_crs = 2248 # The CRS we recieve our data.
out_crs = 4326 # The CRS we would like to have our data represented as.
geom = 'geometry' # The column where our spatial information lives.

# A Url to load
boundariesBaltimoreTractsNoWater2010 = "https://docs.google.com/spreadsheets/d/e/2PACX-1vQ8xXdUaT17jkdK0MWTJpg3GOy6jMWeaXTlguXNjCSb8Vr_FanSZQRaTU-m811fQz4kyMFK5wcahMNY/pub?gid=886223646&single=true&output=csv"

# Read in the dataframe
gdf = pd.read_csv(boundariesBaltimoreTractsNoWater2010)

# Convert the geometry column datatype from a string of text into a coordinate datatype.
gdf['geometry'] = gdf['geometry'].apply(lambda x: loads( str(x) ))

# Process the dataframe as a geodataframe with a known CRS and geom column.
gdf = GeoDataFrame(gdf, crs=in_crs, geometry='geometry')boundariesBaltimoreTractsNoWater2010.head()<p>Ensure merge is on consistent datatypes</p>
gdf['GEOID10'] = gdf['GEOID10'].astype(str)
scooterdf['nameChange2'] = scooterdf['nameChange2'].astype(str)<p>Perform the merge</p>
scooterdfClean = gdf.merge(scooterdf, left_on='GEOID10', right_on='nameChange2').drop(['name', 'nameChange1', 'nameChange2'], axis=1)scooterdfClean.head()# scooterdf.to_csv('./scooterdf.csv', index=False)
# gdf.drop(columns='geometry').to_csv('./boundsdf.csv', index=False)lsscooterdfClean.groupby('CSA')['value'].sum()scooterdfClean.value.isna().groupby([scooterdfClean['CSA']]).sum().astype(int).reset_index(name='notApplicable')scooterdfClean.value.notnull().groupby([scooterdfClean['CSA']]).sum().astype(int).reset_index(name='NotMissingCount')scooterdfClean.value.isnull().groupby([scooterdfClean['CSA']]).sum().astype(int).reset_index(name='NumberMissingCount')scooterdfClean.fillna(-1).groupby('CSA')['value'].sum()scooterdfClean.groupby('CSA')['value'].mean()scooterdfClean.groupby('CSA')['value'].sum()scooterdfClean.CSA.value_counts()https://pandas.pydata.org/docs/getting_started/intro_tutorials/06_calculate_statistics.html<h2>Inspect Routes Data</h2>
fileName = 'Routing December 2019.geojson'  #@param ['Routing August 2019.geojson', 'Routing October 2019.geojson', 'Routing December 2019.geojson', 'Routing September 2019.geojson', 'Routing November 2019.geojson']
columnName = "streetname"  #@param ['id', 'color', 'streetname', 'trip_count_sum', 'trip_count_average', 'trip_count_percent']
gdf = gpd.read_file( findFile('./', fileName) )
gdf.head()<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>id</th>
      <th>color</th>
      <th>streetname</th>
      <th>trip_count_sum</th>
      <th>trip_count_average</th>
      <th>trip_count_percent</th>
      <th>geometry</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>150197772</td>
      <td>rgb(218, 231, 241)</td>
      <td>Jefferson Street</td>
      <td>24</td>
      <td>0.774194</td>
      <td>0.0%</td>
      <td>LINESTRING (-76.58576 39.29660, -76.58550 39.29661)</td>
    </tr>
    <tr>
      <th>1</th>
      <td>150155955</td>
      <td>rgb(164, 190, 219)</td>
      <td></td>
      <td>206</td>
      <td>6.645161</td>
      <td>0.3%</td>
      <td>LINESTRING (-76.61320 39.28136, -76.61318 39.28115)</td>
    </tr>
    <tr>
      <th>2</th>
      <td>150191673</td>
      <td>rgb(204, 221, 236)</td>
      <td>Harford Avenue</td>
      <td>49</td>
      <td>1.580645</td>
      <td>0.1%</td>
      <td>LINESTRING (-76.60169 39.30535, -76.60163 39.30544, -76.60156 39.30555)</td>
    </tr>
    <tr>
      <th>3</th>
      <td>150184657</td>
      <td>rgb(229, 239, 246)</td>
      <td></td>
      <td>11</td>
      <td>0.354839</td>
      <td>0.0%</td>
      <td>LINESTRING (-76.61493 39.29294, -76.61491 39.29258, -76.61490 39.29230)</td>
    </tr>
    <tr>
      <th>4</th>
      <td>150188407</td>
      <td>rgb(169, 194, 221)</td>
      <td></td>
      <td>178</td>
      <td>5.741935</td>
      <td>0.2%</td>
      <td>LINESTRING (-76.61662 39.29053, -76.61661 39.29046)</td>
    </tr>
  </tbody>
</table>
</div>baltimore = gpd.read_file("https://opendata.arcgis.com/datasets/b738a8587b6d479a8824d937892701d8_0.geojson")tst = """background = alt.Chart(gdf).mark_geoshape(
    stroke='white',
    strokeWidth=2
).encode(
    color=alt.value('#eee'),
).properties(
    width=700,
    height=500
)"""

# GeoDataFrame could be passed as usual pd.DataFrame
city = alt.Chart(baltimore).mark_geoshape(
).project(
).encode(
    color='tpop10', # shorthand infer types as for regular pd.DataFrame
    tooltip='CSA2010' # GeoDataFrame.index is accessible as id
).properties(
    width=500,
    height=300
)

routes = alt.Chart(gdf).mark_geoshape(
    filled=False,
    strokeWidth=2
)

city + routes<p>Clean the gdf of empties.</p>
gdf = gdf[~gdf.isna()]
gdf = gdf[~gdf.is_empty]<p>Now get the extremities; this will take a minute.</p>
# hide_output
gdf['lefty'], gdf['leftx'],gdf['righty'], gdf['rightx'] = zip(*gdf["geometry"].map(split))<p>Split the gdf into a left and right dataset</p>
gdf_left = gdf.copy()
gdf_left = gdf_left.drop(columns = ['geometry','rightx', 'righty'])

gdf_right= gdf.copy()
gdf_right = gdf_right.drop(columns = ['geometry','leftx', 'lefty', 'streetname', 'trip_count_sum', 'trip_count_average', 'trip_count_percent', 'color' ])<p>The coordinate variables of object type will cause problems.</p>
gdf_right.dtypesid        int64  
righty    float64
rightx    object 
dtype: object<p>Let's go ahead and coerce a correction.</p>
gdf_right['righty']=pd.to_numeric(gdf_right['righty'], errors='coerce')
gdf_left['lefty']=pd.to_numeric(gdf_left['lefty'], errors='coerce')

gdf_right['rightx']=pd.to_numeric(gdf_right['rightx'], errors='coerce')
gdf_left['leftx']=pd.to_numeric(gdf_left['leftx'], errors='coerce')<p>Now we can view the results</p>
gdf_right.dtypesid        int64  
righty    float64
rightx    float64
dtype: object<p>Save these csv's because it took a minute to get to where we are now.</p>
gdf_right.to_csv('rightRouts.csv')
gdf_left.to_csv('leftRouts.csv')<p>Convert the datasets to geodataframes for further exploration!</p>
# We could create a geodataframe like this
#temp_gdf = gpd.GeoDataFrame( gdf_right, geometry=gpd.points_from_xy(gdf_right.rightx, gdf_right.righty) )
#temp_gdf.head()

# Alternately this could work.. unfinished.. but wkt.loads can make a Point from text
# gdf_right['strCol']=gdf_right['rightx'].astype(str)
# gdf_right['geometry'] = gdf_right['strCol'].apply(wkt.loads)# hide_output
left_csaMap = readInGeometryData(url=gdf_left, porg='p', geom= False, lat= 'leftx', lng= 'lefty', revgeocode=False, save=False, in_crs=4268, out_crs=4268)# hide_output
right_csaMap = readInGeometryData(url=gdf_right, porg='p', geom= False, lat= 'rightx', lng= 'righty', revgeocode=False, save=False, in_crs=4268, out_crs=4268)right_csaMap.head()<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>id</th>
      <th>righty</th>
      <th>rightx</th>
      <th>geometry</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>150197772</td>
      <td>-76.585503</td>
      <td>39.296607</td>
      <td>POINT (-76.58550 39.29661)</td>
    </tr>
    <tr>
      <th>1</th>
      <td>150155955</td>
      <td>-76.613183</td>
      <td>39.281147</td>
      <td>POINT (-76.61318 39.28115)</td>
    </tr>
    <tr>
      <th>2</th>
      <td>150191673</td>
      <td>-76.601555</td>
      <td>39.305545</td>
      <td>POINT (-76.60156 39.30555)</td>
    </tr>
    <tr>
      <th>3</th>
      <td>150184657</td>
      <td>-76.614899</td>
      <td>39.292301</td>
      <td>POINT (-76.61490 39.29230)</td>
    </tr>
    <tr>
      <th>4</th>
      <td>150188407</td>
      <td>-76.616608</td>
      <td>39.290458</td>
      <td>POINT (-76.61661 39.29046)</td>
    </tr>
  </tbody>
</table>
</div>left_csaMap.head()left_csaMap.plot(column='trip_count_sum')right_csaMap.plot()baltimore.columns# hide_output
right_points_in_poly = getPointsInPolygons(right_csaMap, baltimore, 'geometry', 'geometry')right_csaMap.head()left_points_in_poly = getPointsInPolygons(left_csaMap, baltimore, 'geometry', 'geometry')left_csaMap.head()left_points_in_poly.drop('geometry', axis=1)[['pointsinpolygon']].head(20)<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

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        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>pointsinpolygon</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>18</td>
    </tr>
    <tr>
      <th>1</th>
      <td>0</td>
    </tr>
    <tr>
      <th>2</th>
      <td>67</td>
    </tr>
    <tr>
      <th>3</th>
      <td>0</td>
    </tr>
    <tr>
      <th>4</th>
      <td>1536</td>
    </tr>
    <tr>
      <th>5</th>
      <td>18</td>
    </tr>
    <tr>
      <th>6</th>
      <td>32</td>
    </tr>
    <tr>
      <th>7</th>
      <td>8</td>
    </tr>
    <tr>
      <th>8</th>
      <td>1</td>
    </tr>
    <tr>
      <th>9</th>
      <td>65</td>
    </tr>
    <tr>
      <th>10</th>
      <td>0</td>
    </tr>
    <tr>
      <th>11</th>
      <td>0</td>
    </tr>
    <tr>
      <th>12</th>
      <td>13</td>
    </tr>
    <tr>
      <th>13</th>
      <td>1764</td>
    </tr>
    <tr>
      <th>14</th>
      <td>0</td>
    </tr>
    <tr>
      <th>15</th>
      <td>760</td>
    </tr>
    <tr>
      <th>16</th>
      <td>0</td>
    </tr>
    <tr>
      <th>17</th>
      <td>85</td>
    </tr>
    <tr>
      <th>18</th>
      <td>1253</td>
    </tr>
    <tr>
      <th>19</th>
      <td>2</td>
    </tr>
  </tbody>
</table>
</div>right_points_in_poly.drop('geometry', axis=1)[['pointsinpolygon']].head(20)<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

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        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>pointsinpolygon</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>18</td>
    </tr>
    <tr>
      <th>1</th>
      <td>0</td>
    </tr>
    <tr>
      <th>2</th>
      <td>67</td>
    </tr>
    <tr>
      <th>3</th>
      <td>0</td>
    </tr>
    <tr>
      <th>4</th>
      <td>1536</td>
    </tr>
    <tr>
      <th>5</th>
      <td>18</td>
    </tr>
    <tr>
      <th>6</th>
      <td>32</td>
    </tr>
    <tr>
      <th>7</th>
      <td>8</td>
    </tr>
    <tr>
      <th>8</th>
      <td>1</td>
    </tr>
    <tr>
      <th>9</th>
      <td>65</td>
    </tr>
    <tr>
      <th>10</th>
      <td>0</td>
    </tr>
    <tr>
      <th>11</th>
      <td>0</td>
    </tr>
    <tr>
      <th>12</th>
      <td>13</td>
    </tr>
    <tr>
      <th>13</th>
      <td>1764</td>
    </tr>
    <tr>
      <th>14</th>
      <td>0</td>
    </tr>
    <tr>
      <th>15</th>
      <td>760</td>
    </tr>
    <tr>
      <th>16</th>
      <td>0</td>
    </tr>
    <tr>
      <th>17</th>
      <td>85</td>
    </tr>
    <tr>
      <th>18</th>
      <td>1253</td>
    </tr>
    <tr>
      <th>19</th>
      <td>2</td>
    </tr>
  </tbody>
</table>
</div>left_points_in_poly.plot(column='pointsinpolygon', legend=True)<matplotlib.axes._subplots.AxesSubplot at 0x7fca535fbbe0><Figure size 432x288 with 2 Axes>right_points_in_poly.plot(column='pointsinpolygon', legend=True)<matplotlib.axes._subplots.AxesSubplot at 0x7fca51773978><Figure size 432x288 with 2 Axes>right_points_in_polyscooterdfClean = left_points_in_poly.merge(right_points_in_poly, left_on='CSA2010', right_on='CSA2010', how='left')scooterdfClean.columnsright_points_in_poly.columnsIndex(['OBJECTID', 'CSA2010', 'tpop10', 'male10', 'female10', 'paa17',
       'pwhite17', 'pasi17', 'p2more17', 'ppac17', 'phisp17', 'racdiv17',
       'age5_17', 'age18_17', 'age24_17', 'age64_17', 'age65_17', 'hhs10',
       'femhhs17', 'fam17', 'hhsize10', 'mhhi17', 'hh25inc17', 'hh40inc17',
       'hh60inc17', 'hh75inc17', 'hhm7517', 'hhpov17', 'hhchpov17',
       'Shape__Area', 'Shape__Length', 'geometry', 'pointsinpolygon'],
      dtype='object')right_points_in_poly.head()right_points_in_poly.plot('pointsinpolygon', legend=True)<matplotlib.axes._subplots.AxesSubplot at 0x7fca55083ac8><Figure size 432x288 with 2 Axes>import altair as alt
import geopandas as gpd
import gpdvega
# GeoDataFrame could be passed as usual pd.DataFrame
chart = alt.Chart(right_points_in_poly).mark_geoshape(
).project(
).encode(
    color='pointsinpolygon', # shorthand infer types as for regular pd.DataFrame
    tooltip=['CSA2010','pointsinpolygon'] # GeoDataFrame.index is accessible as id
).properties(
    width=500,
    height=300
) 

routes = alt.Chart(gdf).mark_geoshape(
    filled=False,
    strokeWidth=2
)

chart + routes

chart.save(fileName[:-8]+'.html', embed_options={'renderer':'svg'})
chart.save(fileName[:-8]+'.json')
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0.387096774193548, "trip_count_percent": "0.0%", "trip_count_sum": 12, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.642785, 39.283256], [-76.642075, 39.283294]]}}, {"color": "rgb(205, 222, 236)", "id": 150159710, "streetname": "", "trip_count_average": 1.51612903225806, "trip_count_percent": "0.1%", "trip_count_sum": 47, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.579194, 39.280414], [-76.579178, 39.280235]]}}, {"color": "rgb(194, 214, 232)", "id": 150156733, "streetname": "", "trip_count_average": 2.35483870967742, "trip_count_percent": "0.1%", "trip_count_sum": 73, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.615014, 39.286567], [-76.615006, 39.286544]]}}, {"color": "rgb(217, 231, 241)", "id": 150198087, "streetname": "East Madison Street", "trip_count_average": 0.806451612903226, "trip_count_percent": "0.0%", "trip_count_sum": 25, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.574753, 39.300209], [-76.574555, 39.300216], [-76.573906, 39.300239]]}}, {"color": "rgb(212, 227, 239)", "id": 150153554, "streetname": "Emory Street", "trip_count_average": 1.09677419354839, "trip_count_percent": "0.0%", "trip_count_sum": 34, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.624718, 39.286167], [-76.624703, 39.285686]]}}, {"color": "rgb(192, 212, 231)", "id": 150217367, "streetname": "Perring Parkway", "trip_count_average": 2.58064516129032, "trip_count_percent": "0.1%", "trip_count_sum": 80, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.582177, 39.35028], [-76.581772, 39.350574], [-76.581437, 39.350841]]}}, {"color": "rgb(219, 232, 242)", "id": 150191249, "streetname": "East Chase Street", "trip_count_average": 0.741935483870968, "trip_count_percent": "0.0%", "trip_count_sum": 23, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.607644, 39.302707], [-76.607293, 39.302719]]}}, {"color": "rgb(211, 226, 238)", "id": 150188138, "streetname": "North Frederick Street", "trip_count_average": 1.12903225806452, "trip_count_percent": "0.0%", "trip_count_sum": 35, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.607789, 39.291355], [-76.607728, 39.290618]]}}, {"color": "rgb(194, 213, 231)", "id": 149970366, "streetname": "Reisterstown Road", "trip_count_average": 2.41935483870968, "trip_count_percent": "0.1%", "trip_count_sum": 75, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.700181, 39.354648], [-76.699799, 39.354381]]}}, {"color": "rgb(220, 233, 242)", "id": 150217175, "streetname": "", "trip_count_average": 0.67741935483871, "trip_count_percent": "0.0%", "trip_count_sum": 21, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.582444, 39.344722], [-76.582306, 39.344669], [-76.582131, 39.344589], [-76.581864, 39.34447]]}}, {"color": "rgb(143, 147, 197)", "id": 150156210, "streetname": "", "trip_count_average": 17.2903225806452, "trip_count_percent": "0.7%", "trip_count_sum": 536, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.610001, 39.28083], [-76.609719, 39.280834]]}}, {"color": "rgb(224, 236, 244)", "id": 150184120, "streetname": "North Caroline Street", "trip_count_average": 0.516129032258065, "trip_count_percent": "0.0%", "trip_count_sum": 16, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.59797, 39.304157], [-76.597901, 39.303077]]}}, {"color": "rgb(168, 194, 221)", "id": 150196766, "streetname": "", "trip_count_average": 5.80645161290323, "trip_count_percent": "0.2%", "trip_count_sum": 180, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.618561, 39.327087], [-76.61847, 39.327171], [-76.618432, 39.327201]]}}, {"color": "rgb(169, 194, 221)", "id": 150153354, "streetname": "MD 295 North", "trip_count_average": 5.64516129032258, "trip_count_percent": "0.2%", "trip_count_sum": 175, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.623421, 39.280483], [-76.623353, 39.280635]]}}, {"color": "rgb(152, 172, 210)", "id": 150156452, "streetname": "", "trip_count_average": 10.5806451612903, "trip_count_percent": "0.4%", "trip_count_sum": 328, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.617798, 39.284133], [-76.617806, 39.284065], [-76.617783, 39.283969]]}}, {"color": "rgb(178, 202, 225)", "id": 150162339, "streetname": "Hudson Street", "trip_count_average": 4.2258064516129, "trip_count_percent": "0.2%", "trip_count_sum": 131, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.567833, 39.282516], [-76.567696, 39.28252], [-76.567444, 39.282531]]}}, {"color": "rgb(186, 208, 229)", "id": 150159328, "streetname": "South Washington Street", "trip_count_average": 3.12903225806452, "trip_count_percent": "0.1%", "trip_count_sum": 97, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.58873, 39.284759], [-76.5887, 39.284313]]}}, {"color": "rgb(188, 209, 229)", "id": 150159833, "streetname": "", "trip_count_average": 2.93548387096774, "trip_count_percent": "0.1%", "trip_count_sum": 91, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.581383, 39.281337], [-76.581307, 39.281444]]}}, {"color": "rgb(225, 236, 244)", "id": 150201332, "streetname": "East Preston Street", "trip_count_average": 0.483870967741935, "trip_count_percent": "0.0%", "trip_count_sum": 15, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.582711, 39.305812], [-76.582634, 39.305816], [-76.582215, 39.305831]]}}, {"color": "rgb(186, 208, 229)", "id": 150188519, "streetname": "East Saratoga Street", "trip_count_average": 3.12903225806452, "trip_count_percent": "0.1%", "trip_count_sum": 97, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.615258, 39.292289], [-76.614899, 39.292301]]}}, {"color": "rgb(217, 231, 241)", "id": 149974514, "streetname": "Clipper Park Road", "trip_count_average": 0.806451612903226, "trip_count_percent": "0.0%", "trip_count_sum": 25, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.648209, 39.331081], [-76.647767, 39.331172]]}}, {"color": "rgb(140, 126, 186)", "id": 150195291, "streetname": "Maryland Avenue", "trip_count_average": 24.2903225806452, "trip_count_percent": "1.0%", "trip_count_sum": 753, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.618042, 39.312675], [-76.61802, 39.312164]]}}, {"color": "rgb(232, 242, 247)", "id": 150199324, "streetname": "", "trip_count_average": 0.258064516129032, "trip_count_percent": "0.0%", "trip_count_sum": 8, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.578919, 39.292121], [-76.578911, 39.292041]]}}, {"color": "rgb(207, 223, 237)", "id": 150187820, "streetname": "Dolphin Street", "trip_count_average": 1.35483870967742, "trip_count_percent": "0.1%", "trip_count_sum": 42, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.62262, 39.304103], [-76.622376, 39.304141]]}}, {"color": "rgb(139, 105, 176)", "id": 150155614, "streetname": "", "trip_count_average": 32.4193548387097, "trip_count_percent": "1.3%", "trip_count_sum": 1005, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.610665, 39.280818], [-76.610589, 39.280807], [-76.610245, 39.280803], [-76.6101, 39.280803]]}}, {"color": "rgb(148, 164, 205)", "id": 150188243, "streetname": "", "trip_count_average": 12.6129032258065, "trip_count_percent": "0.5%", "trip_count_sum": 391, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.617661, 39.289268], [-76.617661, 39.289337]]}}, {"color": "rgb(211, 226, 238)", "id": 150159702, "streetname": "South Duncan Street", "trip_count_average": 1.12903225806452, "trip_count_percent": "0.0%", "trip_count_sum": 35, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.586487, 39.286186], [-76.586487, 39.286254]]}}, {"color": "rgb(194, 214, 232)", "id": 150190292, "streetname": "East Madison Street", "trip_count_average": 2.35483870967742, "trip_count_percent": "0.1%", "trip_count_sum": 73, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.612374, 39.298721], [-76.61155, 39.298744]]}}, {"color": "rgb(173, 197, 223)", "id": 150153588, "streetname": "", "trip_count_average": 5.0, "trip_count_percent": "0.2%", "trip_count_sum": 155, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.624459, 39.286258], [-76.624405, 39.286262], [-76.623932, 39.286277], [-76.62362, 39.286289]]}}]}}, {"mode": "vega-lite"});
</script><h2>Inspect Origins-Destinations Data</h2>
ls 'Trip origins-destinations by month'# @title Example form fields
#@markdown Forms support many types of fields.
fileName = 'Trip Destinations by block August 2019.geojson'  #@param ['Trip Destinations by block August 2019.geojson', 'Trip Destinations by block December 2019.geojson', 'Trip Destinations by block November 2019.geojson', 'Trip Destinations by block October 2019.geojson', 'Trip Destinations by block September 2019.geojson', 'Trip Origins by block August 2019.geojson', 'Trip Origins by block December 2019.geojson', 'Trip Origins by block November 2019.geojson', 'Trip Origins by block October 2019.geojson', 'Trip Origins by block September 2019.geojson']
columnName = "name"  #@param ['name', 'value', 'color', 'radius']

#@markdown ---

gdf = gpd.read_file( findFile('./', fileName) )

gdf.plot( column = columnName)
gdf.columns
gdf[['id','name', 'value', 'color', 'radius']].head(5)dxp.bar(x='color', y='value', data=gdf, aggfunc='median')dxp.scatter(x = "color", y = "value", data = gdf, aggfunc = "median")#hide
!pip install nbdev
from nbdev import *
! nbdev_upgrade<h2>Introduction</h2>
<p><a href="https://mybinder.org/v2/gh/karpatic/datalab/master?filepath=%2Fnotebooks%2F02_scooterExploration.ipynb"><img src="https://mybinder.org/badge_logo.svg" alt="Binder" /></a>
<a href="https://colab.research.google.com/github/karpatic/datalab/blob/master/notebooks/02_scooterExploration.ipynb"><img src="https://pete88b.github.io/fastpages/assets/badges/colab.svg" alt="Binder" /></a>
<a href="https://github.com/karpatic/datalab/tree/master/notebooks/02_scooterExploration.ipynb"><img src="https://pete88b.github.io/fastpages/assets/badges/github.svg" alt="Binder" /></a>
<a href="https://github.com/ellerbrock/open-source-badges/"><img src="https://badges.frapsoft.com/os/v3/open-source.svg?v=103" alt="Open Source Love svg3" /></a></p>
<p><a href="https://github.com/karpatic/datalabs/blob/master/LICENSE"><img src="https://img.shields.io/npm/l/all-contributors.svg?style=flat" alt="NPM License" /></a>
<a href="https://karpatic.github.io"><img src="http://img.shields.io/badge/Status-Active-green.svg" alt="Active" /></a>
<a href="https://pypi.python.org/pypi/dataplay/"><img src="https://img.shields.io/pypi/pyversions/dataplay.svg" alt="Python Versions" /></a>
<a href=""><img src="https://img.shields.io/github/last-commit/karpatic/datalabs.svg?style=flat" alt="GitHub last commit" /></a>
<a href="http://unmaintained.tech/"><img src="http://unmaintained.tech/badge.svg" alt="No Maintenance Intended" /></a> </p>
<p><a href="https://github.com/karpatic/datalabs"><img src="https://img.shields.io/github/stars/karpatic/datalabs.svg?style=social&amp;label=Star" alt="GitHub stars" /></a>
<a href="https://github.com/karpatic/datalabs"><img src="https://img.shields.io/github/watchers/karpatic/datalabs.svg?style=social&amp;label=Watch" alt="GitHub watchers" /></a>
<a href="https://github.com/karpatic/datalabs"><img src="https://img.shields.io/github/forks/karpatic/datalabs.svg?style=social&amp;label=Fork" alt="GitHub forks" /></a>
<a href="https://github.com/karpatic/datalabs"><img src="https://img.shields.io/github/followers/karpatic.svg?style=social&amp;label=Follow" alt="GitHub followers" /></a> </p>
<p><a href="https://twitter.com/intent/tweet?text=Check%20out%20this%20%E2%9C%A8%20colab%20by%20@bniajfi%20https://github.com/karpatic/datalabs%20%F0%9F%A4%97"><img src="https://img.shields.io/twitter/url/https/github.com/karpatic/datalabs.svg?style=social" alt="Tweet" /></a>
<a href="https://twitter.com/bniajfi"><img src="https://img.shields.io/twitter/follow/bniajfi.svg?style=social" alt="Twitter Follow" /></a></p>
<p>This chapter has brought to you in collaboration by Brian Kelly, Michael Vandi, Logan Shertz, and Charles Karpati.</p>
<p><strong>Dataset:</strong>
Scooter data: </p>
<ul>
<li>Routes: 3 months (September to August 2019)</li>
<li>Deployment/</li>
<li>Routes</li>
<li>Trip origins-destinations by month</li>
</ul>
# do you see this? - Carlos# Hello world! - Michael# Checking - Brian<h2>Local File Access (Optional)</h2>
# hide_output
# (Optional) Run this cell to gain access to Google Drive (Colabs only) 
from google.colab import drive

# Colabs operates in a virtualized enviornment
# Colabs default directory is at ~/content.
# We mount Drive into a temporary folder at '~/content/drive' 

drive.mount('/content/drive')Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly&response_type=code

Enter your authorization code:
··········
Mounted at /content/drive
<p>File path's will vary</p>
# cd ./drive/'My Drive'/BNIA/responsive_records/Routes# cd ../content/drive/My Drive/DATA/scooter/content/drive/My Drive/DATA/scooter
# Michael's Directory
# cd ./drive/'My Drive'/BNIA/'Scooter Use Data'/BNIAcd ./Routes/content/drive/My Drive/DATA/scooter/Routes
# hide_output
! ls leftRouts.csv			 'Routing November 2019.geojson'
 rightRouts.csv			 'Routing October 2019.geojson'
'Routing August 2019.geojson'	 'Routing September 2019.geojson'
'Routing December 2019.geojson'
# hide_output
!cd ../ && ls boundsdf.csv	 rightRouts.csv  'Trip origins-destinations by month'
 Deployment	 Routes
 leftRouts.csv	 scooterdf.csv
<h1><strong>Installs</strong></h1>
! pip install dexplot
!pip install folium
!pip install geopandas
!pip install ipyleaflet
!pip install gpdvega
!pip install dataplay<h1><strong>Imports</strong></h1>
import os
import pandas as pd
import geopandas as gpd
import dexplot as dxp
import folium as fol
import json
import altair as alt
import gpdvega
import dataplay
# These imports will handle everything
import os
import sys
import csv
from IPython.display import clear_output
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import geopandas as gpd
from geopandas import GeoDataFrame
import psycopg2
import pyproj
from pyproj import Proj, transform
# conda install -c conda-forge proj4
from shapely.geometry import Point
from shapely import wkb
from shapely.wkt import loads
# https://pypi.org/project/geopy/
from geopy.geocoders import Nominatim
import folium
from folium import plugins

from dataplay.merge import mergeDatasets 

import dexplot as dxp

# In case file is KML, enable support
import fiona
fiona.drvsupport.supported_drivers['kml'] = 'rw'
fiona.drvsupport.supported_drivers['KML'] = 'rw'

#this cell is good to copy
from shapely.geometry import LineString
pd.plotting.register_matplotlib_converters()

from dataplay.geoms import readInGeometryData
from shapely import wkt
from dataplay.geoms import readInGeometryData

import ipywidgets as widgets
!jupyter nbextension enable --py widgetsnbextension
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = 'all'
import ipywidgets as widgets
from ipywidgets import interact, interact_manual
alt.data_transformers.enable('default', max_rows=None)<h3>Convenience Functions</h3>
# Return the boundaries of a Linestring 
def split(geom):
  print( str( type(geom)) )
  #Linestring might not be the actual type, so this may need to be fied. 
  #IF its not a linestring then dont run it and stuff 'false' and the datatype
  if str( type(geom)) == "<class 'shapely.geometry.linestring.LineString'>" and not str(geom.boundary) == 'MULTIPOINT EMPTY':
    print(geom.boundary)
    left, right = geom.boundary
    print('left x: ', type(left.x), left.x)
    print('left y: ', type(left.y), left.y)
    print('right x: ', type(right.x), right.x)
    print('right y: ', type(right.y), right.y)
    return left.x, left.y, right.x, right.y
  else: return False, type(geom), False, type(geom)# To 'import' a script you wrote, map its filepath into the sys
def getPointsInPolygons(pts, polygons, ptsCoordCol, polygonsCoordCol): 
    count = 0 
    total = pts.size / len(pts.columns)
    # We're going to keep a list of how many points we find.
    pts_in_polys = []

    # Loop over polygons with index i.
    for i, poly in polygons.iterrows():
        # print('Searching for point within Geom:', poly )
        # Keep a list of points in this poly
        pts_in_this_poly = []

        # Now loop over all points with index j.
        for j, pt in pts.iterrows():
          if poly[polygonsCoordCol].contains(pt[ptsCoordCol]):
            # Then it's a hit! Add it to the list,
            # and drop it so we have less hunting.
            pts_in_this_poly.append(pt[ptsCoordCol])
            pts = pts.drop([j])

        # We could do all sorts, like grab a property of the
        # points, but let's just append the number of them.
        pts_in_polys.append(len(pts_in_this_poly))
        print('Found this many points within the Geom:', len(pts_in_this_poly) ) 
        count = count + len(pts_in_this_poly) 
        clear_output(wait=True)

    # Add the number of points for each poly to the dataframe.
    polygons['pointsinpolygon'] = gpd.GeoSeries(pts_in_polys)
    print( 'Total Points: ', total )
    print( 'Total Points in Polygons: ', count )
    print( 'Prcnt Points in Polygons: ', count / total )
    return polygons<h3>File Access Convenience Functions</h3>
# Find Relative Path to Files
def findFile(root, file):
    for d, subD, f in os.walk(root):
        if file in f:
            return "{1}/{0}".format(file, d)
            break 

# To 'import' a script you wrote, map its filepath into the sys
def addPath(root, file): sys.path.append(os.path.abspath( findFile( './', file) ))findFile('./', 'Routing September 2019.geojson')'.//Routing September 2019.geojson'ls leftRouts.csv                   'Routing November 2019.geojson'
 rightRouts.csv                  'Routing October 2019.geojson'
'Routing August 2019.geojson'    'Routing September 2019.geojson'
'Routing December 2019.geojson'
<h2>Inspect Deployment Data</h2>
ls 'Trip origins-destinations by month''Trip Destinations by block August 2019.geojson'
'Trip Destinations by block December 2019.geojson'
'Trip Destinations by block November 2019.geojson'
'Trip Destinations by block October 2019.geojson'
'Trip Destinations by block September 2019.geojson'
'Trip Origins by block August 2019.geojson'
'Trip Origins by block December 2019.geojson'
'Trip Origins by block November 2019.geojson'
'Trip Origins by block October 2019.geojson'
'Trip Origins by block September 2019.geojson'
#@title Example form fields
#@markdown Forms support many types of fields.
fileName = "Trip Origins by block September 2019.geojson"  #@param ['Daily Deployment average by block August 2019.csv', 'Daily Deployment average by block December 2019.csv', 'Daily Deployment average by block November 2019.csv', 'Daily Deployment average by block October 2019.csv', 'Daily Deployment average by block September 2019.csv', 'Trip Destinations by block August 2019.geojson','Trip Destinations by block December 2019.geojson','Trip Destinations by block November 2019.geojson', 'Trip Destinations by block October 2019.geojson', 'Trip Destinations by block September 2019.geojson', 'Trip Origins by block August 2019.geojson','Trip Origins by block December 2019.geojson','Trip Origins by block November 2019.geojson','Trip Origins by block October 2019.geojson','Trip Origins by block September 2019.geojson']
#@markdown ---gdf = gpd.read_file( findFile('./', fileName) )
gdf.head()<div>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>id</th>
      <th>name</th>
      <th>color</th>
      <th>radius</th>
      <th>value</th>
      <th>geometry</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>1</td>
      <td>Block 245102502063018</td>
      <td>#fff</td>
      <td>4.999286</td>
      <td>NaN</td>
      <td>POLYGON ((-76.66168 39.26342, -76.66136 39.26373, -76.66096 39.26343, -76.66041 39.26306, -76.66053 39.26296, -76.66070 39.26281, -76.66077 39.26284, -76.66100 39.26296, -76.66117 39.26306, -76.66129 39.26315, -76.66141 39.26322, -76.66168 39.26342))</td>
    </tr>
    <tr>
      <th>1</th>
      <td>2</td>
      <td>Block 245102006001022</td>
      <td>#fff</td>
      <td>4.999286</td>
      <td>NaN</td>
      <td>POLYGON ((-76.66319 39.28427, -76.66306 39.28436, -76.66301 39.28439, -76.66293 39.28439, -76.66271 39.28418, -76.66303 39.28402, -76.66317 39.28417, -76.66320 39.28423, -76.66319 39.28427))</td>
    </tr>
    <tr>
      <th>2</th>
      <td>3</td>
      <td>Block 245102804033017</td>
      <td>#fff</td>
      <td>4.999286</td>
      <td>NaN</td>
      <td>POLYGON ((-76.71122 39.27927, -76.71121 39.27975, -76.71121 39.27990, -76.71119 39.28076, -76.71045 39.27932, -76.71045 39.27920, -76.71056 39.27907, -76.71123 39.27880, -76.71122 39.27927))</td>
    </tr>
    <tr>
      <th>3</th>
      <td>4</td>
      <td>Block 245102716006003</td>
      <td>#fff</td>
      <td>4.999286</td>
      <td>NaN</td>
      <td>POLYGON ((-76.66960 39.34512, -76.66905 39.34526, -76.66886 39.34531, -76.66848 39.34539, -76.66835 39.34502, -76.66867 39.34498, -76.66924 39.34484, -76.66946 39.34475, -76.66960 39.34512))</td>
    </tr>
    <tr>
      <th>4</th>
      <td>5</td>
      <td>Block 245102709023013</td>
      <td>#fff</td>
      <td>4.999286</td>
      <td>NaN</td>
      <td>POLYGON ((-76.58976 39.35401, -76.58972 39.35415, -76.58966 39.35435, -76.58964 39.35447, -76.58960 39.35469, -76.58940 39.35467, -76.58925 39.35465, -76.58912 39.35465, -76.58902 39.35464, -76.58898 39.35464, -76.58906 39.35425, -76.58917 39.35385, -76.58925 39.35385, -76.58939 39.35386, -76.58979 39.35393, -76.58976 39.35401))</td>
    </tr>
  </tbody>
</table>
</div>gdf.plot(column = 'value')<matplotlib.axes._subplots.AxesSubplot at 0x7f2c97160da0><Figure size 432x288 with 1 Axes>point_df = gdf.copy()
point_df['centroids'] = df.centroid
point_df = point_df.drop(columns = 'geometry')
point_df = point_df.set_geometry('centroids')
point_df.head(1)
point_df.plot(marker='o', color='red', markersize='value')point_df.value.hist()point_df[point_df.value > 1000].plot()baltimore = gpd.read_file("https://opendata.arcgis.com/datasets/b738a8587b6d479a8824d937892701d8_0.geojson")from dataplay.geoms import workWithGeometryDatabaltimore.columnsIndex(['OBJECTID', 'CSA2010', 'tpop10', 'male10', 'female10', 'paa17',
       'pwhite17', 'pasi17', 'p2more17', 'ppac17', 'phisp17', 'racdiv17',
       'age5_17', 'age18_17', 'age24_17', 'age64_17', 'age65_17', 'hhs10',
       'femhhs17', 'fam17', 'hhsize10', 'mhhi17', 'hh25inc17', 'hh40inc17',
       'hh60inc17', 'hh75inc17', 'hhm7517', 'hhpov17', 'hhchpov17',
       'Shape__Area', 'Shape__Length', 'geometry'],
      dtype='object')pointsInPolys = workWithGeometryData(method = 'ponp', df = point_df, polys = baltimore, ptsCoordCol ='centroids', 
                                     polygonsCoordCol = 'geometry', polygonsLabel = 'CSA2010')
pointsInPolys.plot(column='value', legend=True, markersize = 1)Total Points:  13506.0
Total Points in Polygons:  13453
Prcnt Points in Polygons:  0.9960758181548941
<matplotlib.axes._subplots.AxesSubplot at 0x7ff7c35c92e8><Figure size 432x288 with 2 Axes>ponp_copy = pointsInPolys.copy()ponp_copy.value.isnull().groupby([ponp_copy['CSA2010']]).sum().astype(int).reset_index(name='NumberMissingCount').head()<div>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>CSA2010</th>
      <th>NumberMissingCount</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>Allendale/Irvington/S. Hilton</td>
      <td>290</td>
    </tr>
    <tr>
      <th>1</th>
      <td>Beechfield/Ten Hills/West Hills</td>
      <td>180</td>
    </tr>
    <tr>
      <th>2</th>
      <td>Belair-Edison</td>
      <td>250</td>
    </tr>
    <tr>
      <th>3</th>
      <td>Brooklyn/Curtis Bay/Hawkins Point</td>
      <td>580</td>
    </tr>
    <tr>
      <th>4</th>
      <td>Canton</td>
      <td>0</td>
    </tr>
  </tbody>
</table>
</div>ponp_copy.value.notnull().groupby([ponp_copy['CSA2010']]).sum().astype(int) / ponp_copy.value.isnull().groupby([ponp_copy['CSA2010']]).sum().astype(int)ponp_copy.value.isnull().groupby([ponp_copy['CSA2010']]).sum().astype(int) / ponp_copy.value.groupby([ponp_copy['CSA2010']]).sum().astype(int) * 100ponp_copy.fillna(-1).groupby('CSA2010')['value'].sum()gdf.value.unique()gdf.value.describe()count    5677.000000
mean     40.862603  
std      175.769408 
min      1.000000   
25%      2.000000   
50%      5.000000   
75%      21.000000  
max      6978.000000
Name: value, dtype: float64gdf.value.var() # Return unbiased variance over requested axis.30894.884924369213gdf.value.sem() # Return unbiased standard error of the mean over requested axis.2.332834040372481gdf.value.nunique()	# Count distinct observations over requested axis.364# DataFrame.shape	Return a tuple representing the dimensionality of the DataFrame.
gdf.shape

#DataFrame.size	Return an int representing the number of elements in this object.
gdf.size

# DataFrame.ndim	Return an integer representing the number of axes/array dimensions.
gdf.ndim

# Note Used : 
# DataFrame.axes	Return a list representing the axes of the DataFrame.

gdf.dtypes

# Return unbiased kurtosis over requested axis using Fisher’s definition of kurtosis (kurtosis of normal == 0.0).
gdf.kurtosis()(13506, 6)810362id          object  
name        object  
color       object  
radius      float64 
value       float64 
geometry    geometry
dtype: objectid       -1.200000   
radius    1132.851562
value     495.784213 
dtype: float64<h3>Load Tracts</h3>
# This will just beautify the output.

pd.set_option('display.expand_frame_repr', False)
pd.set_option('display.precision', 2)
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"

pd.set_option('max_colwidth', 20)# The attributes are what we will use.
in_crs = 2248 # The CRS we recieve our data.
out_crs = 4326 # The CRS we would like to have our data represented as.
geom = 'geometry' # The column where our spatial information lives.

# A Url to load
boundariesBaltimoreTractsNoWater2010 = "https://docs.google.com/spreadsheets/d/e/2PACX-1vQ8xXdUaT17jkdK0MWTJpg3GOy6jMWeaXTlguXNjCSb8Vr_FanSZQRaTU-m811fQz4kyMFK5wcahMNY/pub?gid=886223646&single=true&output=csv"

# Read in the dataframe
gdf = pd.read_csv(boundariesBaltimoreTractsNoWater2010)

# Convert the geometry column datatype from a string of text into a coordinate datatype.
gdf['geometry'] = gdf['geometry'].apply(lambda x: loads( str(x) ))

# Process the dataframe as a geodataframe with a known CRS and geom column.
gdf = GeoDataFrame(gdf, crs=in_crs, geometry='geometry')boundariesBaltimoreTractsNoWater2010.head()<p>Ensure merge is on consistent datatypes</p>
gdf['GEOID10'] = gdf['GEOID10'].astype(str)
scooterdf['nameChange2'] = scooterdf['nameChange2'].astype(str)<p>Perform the merge</p>
scooterdfClean = gdf.merge(scooterdf, left_on='GEOID10', right_on='nameChange2').drop(['name', 'nameChange1', 'nameChange2'], axis=1)scooterdfClean.head()# scooterdf.to_csv('./scooterdf.csv', index=False)
# gdf.drop(columns='geometry').to_csv('./boundsdf.csv', index=False)lsscooterdfClean.groupby('CSA')['value'].sum()scooterdfClean.value.isna().groupby([scooterdfClean['CSA']]).sum().astype(int).reset_index(name='notApplicable')scooterdfClean.value.notnull().groupby([scooterdfClean['CSA']]).sum().astype(int).reset_index(name='NotMissingCount')scooterdfClean.value.isnull().groupby([scooterdfClean['CSA']]).sum().astype(int).reset_index(name='NumberMissingCount')scooterdfClean.fillna(-1).groupby('CSA')['value'].sum()scooterdfClean.groupby('CSA')['value'].mean()scooterdfClean.groupby('CSA')['value'].sum()scooterdfClean.CSA.value_counts()https://pandas.pydata.org/docs/getting_started/intro_tutorials/06_calculate_statistics.html<h2>Inspect Routes Data</h2>
fileName = 'Routing December 2019.geojson'  #@param ['Routing August 2019.geojson', 'Routing October 2019.geojson', 'Routing December 2019.geojson', 'Routing September 2019.geojson', 'Routing November 2019.geojson']
columnName = "streetname"  #@param ['id', 'color', 'streetname', 'trip_count_sum', 'trip_count_average', 'trip_count_percent']
gdf = gpd.read_file( findFile('./', fileName) )
gdf.head()<div>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>id</th>
      <th>color</th>
      <th>streetname</th>
      <th>trip_count_sum</th>
      <th>trip_count_average</th>
      <th>trip_count_percent</th>
      <th>geometry</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>150197772</td>
      <td>rgb(218, 231, 241)</td>
      <td>Jefferson Street</td>
      <td>24</td>
      <td>0.774194</td>
      <td>0.0%</td>
      <td>LINESTRING (-76.58576 39.29660, -76.58550 39.29661)</td>
    </tr>
    <tr>
      <th>1</th>
      <td>150155955</td>
      <td>rgb(164, 190, 219)</td>
      <td></td>
      <td>206</td>
      <td>6.645161</td>
      <td>0.3%</td>
      <td>LINESTRING (-76.61320 39.28136, -76.61318 39.28115)</td>
    </tr>
    <tr>
      <th>2</th>
      <td>150191673</td>
      <td>rgb(204, 221, 236)</td>
      <td>Harford Avenue</td>
      <td>49</td>
      <td>1.580645</td>
      <td>0.1%</td>
      <td>LINESTRING (-76.60169 39.30535, -76.60163 39.30544, -76.60156 39.30555)</td>
    </tr>
    <tr>
      <th>3</th>
      <td>150184657</td>
      <td>rgb(229, 239, 246)</td>
      <td></td>
      <td>11</td>
      <td>0.354839</td>
      <td>0.0%</td>
      <td>LINESTRING (-76.61493 39.29294, -76.61491 39.29258, -76.61490 39.29230)</td>
    </tr>
    <tr>
      <th>4</th>
      <td>150188407</td>
      <td>rgb(169, 194, 221)</td>
      <td></td>
      <td>178</td>
      <td>5.741935</td>
      <td>0.2%</td>
      <td>LINESTRING (-76.61662 39.29053, -76.61661 39.29046)</td>
    </tr>
  </tbody>
</table>
</div>baltimore = gpd.read_file("https://opendata.arcgis.com/datasets/b738a8587b6d479a8824d937892701d8_0.geojson")tst = """background = alt.Chart(gdf).mark_geoshape(
    stroke='white',
    strokeWidth=2
).encode(
    color=alt.value('#eee'),
).properties(
    width=700,
    height=500
)"""

# GeoDataFrame could be passed as usual pd.DataFrame
city = alt.Chart(baltimore).mark_geoshape(
).project(
).encode(
    color='tpop10', # shorthand infer types as for regular pd.DataFrame
    tooltip='CSA2010' # GeoDataFrame.index is accessible as id
).properties(
    width=500,
    height=300
)

routes = alt.Chart(gdf).mark_geoshape(
    filled=False,
    strokeWidth=2
)

city + routes<p>Clean the gdf of empties.</p>
gdf = gdf[~gdf.isna()]
gdf = gdf[~gdf.is_empty]<p>Now get the extremities; this will take a minute.</p>
# hide_output
gdf['lefty'], gdf['leftx'],gdf['righty'], gdf['rightx'] = zip(*gdf["geometry"].map(split))<p>Split the gdf into a left and right dataset</p>
gdf_left = gdf.copy()
gdf_left = gdf_left.drop(columns = ['geometry','rightx', 'righty'])

gdf_right= gdf.copy()
gdf_right = gdf_right.drop(columns = ['geometry','leftx', 'lefty', 'streetname', 'trip_count_sum', 'trip_count_average', 'trip_count_percent', 'color' ])<p>The coordinate variables of object type will cause problems.</p>
gdf_right.dtypesid        int64  
righty    float64
rightx    object 
dtype: object<p>Let's go ahead and coerce a correction.</p>
gdf_right['righty']=pd.to_numeric(gdf_right['righty'], errors='coerce')
gdf_left['lefty']=pd.to_numeric(gdf_left['lefty'], errors='coerce')

gdf_right['rightx']=pd.to_numeric(gdf_right['rightx'], errors='coerce')
gdf_left['leftx']=pd.to_numeric(gdf_left['leftx'], errors='coerce')<p>Now we can view the results</p>
gdf_right.dtypesid        int64  
righty    float64
rightx    float64
dtype: object<p>Save these csv's because it took a minute to get to where we are now.</p>
gdf_right.to_csv('rightRouts.csv')
gdf_left.to_csv('leftRouts.csv')<p>Convert the datasets to geodataframes for further exploration!</p>
# We could create a geodataframe like this
#temp_gdf = gpd.GeoDataFrame( gdf_right, geometry=gpd.points_from_xy(gdf_right.rightx, gdf_right.righty) )
#temp_gdf.head()

# Alternately this could work.. unfinished.. but wkt.loads can make a Point from text
# gdf_right['strCol']=gdf_right['rightx'].astype(str)
# gdf_right['geometry'] = gdf_right['strCol'].apply(wkt.loads)# hide_output
left_csaMap = readInGeometryData(url=gdf_left, porg='p', geom= False, lat= 'leftx', lng= 'lefty', revgeocode=False, save=False, in_crs=4268, out_crs=4268)# hide_output
right_csaMap = readInGeometryData(url=gdf_right, porg='p', geom= False, lat= 'rightx', lng= 'righty', revgeocode=False, save=False, in_crs=4268, out_crs=4268)right_csaMap.head()<div>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>id</th>
      <th>righty</th>
      <th>rightx</th>
      <th>geometry</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>150197772</td>
      <td>-76.585503</td>
      <td>39.296607</td>
      <td>POINT (-76.58550 39.29661)</td>
    </tr>
    <tr>
      <th>1</th>
      <td>150155955</td>
      <td>-76.613183</td>
      <td>39.281147</td>
      <td>POINT (-76.61318 39.28115)</td>
    </tr>
    <tr>
      <th>2</th>
      <td>150191673</td>
      <td>-76.601555</td>
      <td>39.305545</td>
      <td>POINT (-76.60156 39.30555)</td>
    </tr>
    <tr>
      <th>3</th>
      <td>150184657</td>
      <td>-76.614899</td>
      <td>39.292301</td>
      <td>POINT (-76.61490 39.29230)</td>
    </tr>
    <tr>
      <th>4</th>
      <td>150188407</td>
      <td>-76.616608</td>
      <td>39.290458</td>
      <td>POINT (-76.61661 39.29046)</td>
    </tr>
  </tbody>
</table>
</div>left_csaMap.head()left_csaMap.plot(column='trip_count_sum')right_csaMap.plot()baltimore.columns# hide_output
right_points_in_poly = getPointsInPolygons(right_csaMap, baltimore, 'geometry', 'geometry')right_csaMap.head()left_points_in_poly = getPointsInPolygons(left_csaMap, baltimore, 'geometry', 'geometry')left_csaMap.head()left_points_in_poly.drop('geometry', axis=1)[['pointsinpolygon']].head(20)<div>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>pointsinpolygon</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>18</td>
    </tr>
    <tr>
      <th>1</th>
      <td>0</td>
    </tr>
    <tr>
      <th>2</th>
      <td>67</td>
    </tr>
    <tr>
      <th>3</th>
      <td>0</td>
    </tr>
    <tr>
      <th>4</th>
      <td>1536</td>
    </tr>
    <tr>
      <th>5</th>
      <td>18</td>
    </tr>
    <tr>
      <th>6</th>
      <td>32</td>
    </tr>
    <tr>
      <th>7</th>
      <td>8</td>
    </tr>
    <tr>
      <th>8</th>
      <td>1</td>
    </tr>
    <tr>
      <th>9</th>
      <td>65</td>
    </tr>
    <tr>
      <th>10</th>
      <td>0</td>
    </tr>
    <tr>
      <th>11</th>
      <td>0</td>
    </tr>
    <tr>
      <th>12</th>
      <td>13</td>
    </tr>
    <tr>
      <th>13</th>
      <td>1764</td>
    </tr>
    <tr>
      <th>14</th>
      <td>0</td>
    </tr>
    <tr>
      <th>15</th>
      <td>760</td>
    </tr>
    <tr>
      <th>16</th>
      <td>0</td>
    </tr>
    <tr>
      <th>17</th>
      <td>85</td>
    </tr>
    <tr>
      <th>18</th>
      <td>1253</td>
    </tr>
    <tr>
      <th>19</th>
      <td>2</td>
    </tr>
  </tbody>
</table>
</div>right_points_in_poly.drop('geometry', axis=1)[['pointsinpolygon']].head(20)<div>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>pointsinpolygon</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>18</td>
    </tr>
    <tr>
      <th>1</th>
      <td>0</td>
    </tr>
    <tr>
      <th>2</th>
      <td>67</td>
    </tr>
    <tr>
      <th>3</th>
      <td>0</td>
    </tr>
    <tr>
      <th>4</th>
      <td>1536</td>
    </tr>
    <tr>
      <th>5</th>
      <td>18</td>
    </tr>
    <tr>
      <th>6</th>
      <td>32</td>
    </tr>
    <tr>
      <th>7</th>
      <td>8</td>
    </tr>
    <tr>
      <th>8</th>
      <td>1</td>
    </tr>
    <tr>
      <th>9</th>
      <td>65</td>
    </tr>
    <tr>
      <th>10</th>
      <td>0</td>
    </tr>
    <tr>
      <th>11</th>
      <td>0</td>
    </tr>
    <tr>
      <th>12</th>
      <td>13</td>
    </tr>
    <tr>
      <th>13</th>
      <td>1764</td>
    </tr>
    <tr>
      <th>14</th>
      <td>0</td>
    </tr>
    <tr>
      <th>15</th>
      <td>760</td>
    </tr>
    <tr>
      <th>16</th>
      <td>0</td>
    </tr>
    <tr>
      <th>17</th>
      <td>85</td>
    </tr>
    <tr>
      <th>18</th>
      <td>1253</td>
    </tr>
    <tr>
      <th>19</th>
      <td>2</td>
    </tr>
  </tbody>
</table>
</div>left_points_in_poly.plot(column='pointsinpolygon', legend=True)<matplotlib.axes._subplots.AxesSubplot at 0x7fca535fbbe0><Figure size 432x288 with 2 Axes>right_points_in_poly.plot(column='pointsinpolygon', legend=True)<matplotlib.axes._subplots.AxesSubplot at 0x7fca51773978><Figure size 432x288 with 2 Axes>right_points_in_polyscooterdfClean = left_points_in_poly.merge(right_points_in_poly, left_on='CSA2010', right_on='CSA2010', how='left')scooterdfClean.columnsright_points_in_poly.columnsIndex(['OBJECTID', 'CSA2010', 'tpop10', 'male10', 'female10', 'paa17',
       'pwhite17', 'pasi17', 'p2more17', 'ppac17', 'phisp17', 'racdiv17',
       'age5_17', 'age18_17', 'age24_17', 'age64_17', 'age65_17', 'hhs10',
       'femhhs17', 'fam17', 'hhsize10', 'mhhi17', 'hh25inc17', 'hh40inc17',
       'hh60inc17', 'hh75inc17', 'hhm7517', 'hhpov17', 'hhchpov17',
       'Shape__Area', 'Shape__Length', 'geometry', 'pointsinpolygon'],
      dtype='object')right_points_in_poly.head()right_points_in_poly.plot('pointsinpolygon', legend=True)<matplotlib.axes._subplots.AxesSubplot at 0x7fca55083ac8><Figure size 432x288 with 2 Axes>import altair as alt
import geopandas as gpd
import gpdvega
# GeoDataFrame could be passed as usual pd.DataFrame
chart = alt.Chart(right_points_in_poly).mark_geoshape(
).project(
).encode(
    color='pointsinpolygon', # shorthand infer types as for regular pd.DataFrame
    tooltip=['CSA2010','pointsinpolygon'] # GeoDataFrame.index is accessible as id
).properties(
    width=500,
    height=300
) 

routes = alt.Chart(gdf).mark_geoshape(
    filled=False,
    strokeWidth=2
)

chart + routes

chart.save(fileName[:-8]+'.html', embed_options={'renderer':'svg'})
chart.save(fileName[:-8]+'.json')
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{"type": "LineString", "coordinates": [[-76.586487, 39.286186], [-76.586487, 39.286254]]}}, {"color": "rgb(194, 214, 232)", "id": 150190292, "streetname": "East Madison Street", "trip_count_average": 2.35483870967742, "trip_count_percent": "0.1%", "trip_count_sum": 73, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.612374, 39.298721], [-76.61155, 39.298744]]}}, {"color": "rgb(173, 197, 223)", "id": 150153588, "streetname": "", "trip_count_average": 5.0, "trip_count_percent": "0.2%", "trip_count_sum": 155, "type": "Feature", "geometry": {"type": "LineString", "coordinates": [[-76.624459, 39.286258], [-76.624405, 39.286262], [-76.623932, 39.286277], [-76.62362, 39.286289]]}}]}}, {"mode": "vega-lite"});
</script><h2>Inspect Origins-Destinations Data</h2>
ls 'Trip origins-destinations by month'# @title Example form fields
#@markdown Forms support many types of fields.
fileName = 'Trip Destinations by block August 2019.geojson'  #@param ['Trip Destinations by block August 2019.geojson', 'Trip Destinations by block December 2019.geojson', 'Trip Destinations by block November 2019.geojson', 'Trip Destinations by block October 2019.geojson', 'Trip Destinations by block September 2019.geojson', 'Trip Origins by block August 2019.geojson', 'Trip Origins by block December 2019.geojson', 'Trip Origins by block November 2019.geojson', 'Trip Origins by block October 2019.geojson', 'Trip Origins by block September 2019.geojson']
columnName = "name"  #@param ['name', 'value', 'color', 'radius']

#@markdown ---

gdf = gpd.read_file( findFile('./', fileName) )

gdf.plot( column = columnName)
gdf.columns
gdf[['id','name', 'value', 'color', 'radius']].head(5)dxp.bar(x='color', y='value', data=gdf, aggfunc='median')dxp.scatter(x = "color", y = "value", data = gdf, aggfunc = "median")